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Abstract The problem of plant diseases is huge as it affects the crop quality and leads to reduced crop production. Crop-Convolutional neural network (CNN) depiction is that several scholars have used the approaches of machine learning (ML) and deep learning (DL) techniques and have configured their models to specific crops to diagnose plant diseases. In this logic, it is unjustifiable to apply crop-specific models as farmers are resource-poor and possess a low digital literacy level. This study presents a Slender-CNN model of plant disease detection in corn (C), rice (R) and wheat (W) crops. The designed architecture incorporates parallel convolution layers of different dimensions in order to localize the lesions with multiple scales accurately. The experimentation results show that the designed network achieves the accuracy of 88.54% as well as overcomes several benchmark CNN models: VGG19, EfficientNetb6, ResNeXt, DenseNet201, AlexNet, YOLOv5 and MobileNetV3. In addition, the validated model demonstrates its effectiveness as a multi-purpose device by correctly categorizing the healthy and the infected class of individual types of crops, providing 99.81%, 87.11%, and 98.45% accuracy for CRW crops, respectively. Furthermore, considering the best performance values achieved and compactness of the proposed model, it can be employed for on-farm agricultural diseased crops identification finding applications even in resource-limited settings.

Early diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them.

Abstract Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs). This method allows the model to focus on relevant spatial features while capturing sequential relationships in time-series data. The approach uses attention mechanisms to dynamically weight geographic features and LSTM layers to model temporal patterns, resulting in enhanced predictive accuracy. Evaluations using a real-world multi-modal urban transportation dataset demonstrate the performance of GT-LSTM, with significant reductions of 15% in Mean Absolute Percentage Error (MAPE) and 20% in Root Mean Square Error (RMSE) compared to traditional methods. The model also shows substantial improvements over traditional techniques, including Convolutional LSTM and Graph Convolutional Networks. The effectiveness of GT-LSTM in capturing both spatial and temporal dynamics highlights its potential for real-time urban mobility prediction and provides valuable insights for urban planners, policymakers, and transportation authorities to improve decision-making and system efficiency.

Abstract Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Big data contains vast amounts of information that can be processed in the future, leading to improved prediction outcomes. In the realm of big healthcare data, analyzing and preventing hospital errors, along with disease prevention and cost savings, are crucial. Healthcare data analysts play a pivotal role in enhancing healthcare outcomes by gathering, integrating, and analyzing data from diverse sources. Disease diagnosis involves identifying the disease or condition underlying a person's symptoms and signs. Ensemble learning methods, utilized in science and technology, have introduced various classification and clustering techniques to achieve more accurate and time-efficient disease diagnosis. To address these challenges, we introduce the Adamic–Adar Similarity Indexed Wald Boost Data Classification (AASIWBDC) Technique. This technique conducts feature selection and data classification to enhance the efficiency of diabetic disease diagnosis. It utilizes the Adamic–Adar Similarity Index to gauge the similarity between features and their objectives. Leveraging selected features, the AASIWBDC Technique implements the Wald Boost Classification Process to categorize patient data into normal and abnormal groups with greater accuracy. As a result, it achieves effective diabetic disease diagnosis in an efficient manner. We evaluate the performance of the AASIWBDC Technique in terms of diabetic disease diagnosis accuracy, diagnosis time, and error rate using the Diabetes 130-US hospitals dataset from 1999–2008. Experimental results illustrate that the AASIWBDC Technique enhances diabetic disease diagnosis accuracy, reduces diagnosis time, and lowers error rates compared to conventional methods.

Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in early diagnosis and treatment. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool in the field of medical image analysis, revolutionizing the way cancer is detected and diagnosed. The study discusses the various modalities employed in lung cancer diagnosis, such as medical imaging (e.g., radiology and pathology), genomics, and clinical data, highlighting the unique challenges associated with each domain. The proposed Multimodal Fusion Deep Neural Network (MFDNN) architecture design effectively integrates information from different modalities (e.g., medical imaging, genomics, clinical data) to enhance lung cancer diagnostic accuracy. Furthermore, it delves into the integration of clinical data, electronic health records, and multimodal approaches to improve the accuracy and reliability of lung cancer diagnosis. Moreover, we highlight the ethical considerations surrounding the deployment of Artificial Intelligence (AI) in clinical settings and the need for robust validation and regulatory guidelines. The Multimodal Fusion Deep Neural Network (MFDNN) achieves an exceptional accuracy rate of 92.5 %, marking a significant breakthrough in the realm of medical AI. MFDNN excels in precision, with 87.4 % accuracy in predicting cancer cases, and equally impresses in recall, capturing approximately 86.4 % of actual cancerous cases. The F1-score of 86.2 further exemplifies MFDNN's ability to strike a harmonious equilibrium, ensuring both diagnostic accuracy and minimized missed diagnoses. The performance is compared with established methods like CNN, DNN, and ResNet. The results underscore MFDNN's pivotal role in revolutionizing lung cancer diagnosis, promising more accurate and timely identification of this critical condition.

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

Abstract The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

Abstract Parkinson’s Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80–20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson’s Disease.

Abstract Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists’ expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.

The surge in Distributed Denial of Service (DDoS) attacks within SDN environments demands more potent defense strategies. While Moving Target Defense (MTD) holds promise, current MTD approaches against DDoS suffer from security gaps due to overwhelming malicious traffic and static detection areas. In order to tackle these difficulties, we have implemented BSDN-HMTD, a combination of deep learning and blockchain technologies within SDN environments, as a framework. Our strategy starts by employing blockchain technology to authenticate users. We use the NTRU-based Nyberg Rueppel Digital Signature Algorithm for this purpose. This ensures that only authenticated user flows are allowed for validation and forwarding. Within the forwarding layer, Quantum Convolutional Neural Networks (QCNN) evaluate authentic flows by analyzing many characteristics, effectively differentiating between regular, malicious, and dubious flows. Utilizing an Enhanced Spotted Hyena Optimization (EHSO) method to activate switches in real-time modifies the vulnerable points of attack, so impeding attackers and simultaneously decreasing energy usage. The Forwarding Layer Organizer (FLO) oversees the detection of possible attacker surveillance activities and transmits the collected information to local controllers in the control layer. The controllers, functioning in a structured controller network, carry out proactive Moving Target Defense (MTD) techniques, such as host virtual IP hopping, which make attacker plans more complex and raise their operational expenses. Reactive MTD actions are implemented based on the results of flow validation. These actions utilize techniques such as secure honeypots and host virtual IP hopping to effectively prevent attacks. The blockchain securely logs all processed data related to packet validation, authentication, and honeypot activities to ensure the protection of data privacy. Our studies, conducted using Network Simulator-3.26 (NS-3.26), show that our proposed framework outperforms existing techniques in terms of several validation criteria.

Abstract Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Abstract Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy.

Abstract Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI “Magnetic Resonance Imaging” brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min–max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min–max NN, and kernel-based support vector machine, respectively.

Abstract Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Abstract Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543 ms and computational time of 5.987 ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.

Abstract Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.

Abstract Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network, and its architecture is the most prevalent in the medical imaging society. Experiments conducted on difficult datasets directed us to the conclusion that the traditional U-Net framework appears to be deficient in certain respects, despite its overall excellence in segmenting multimodal medical images. Therefore, we propose several modifications to the existing cutting-edge U-Net model. The technical approach involves applying a Multi-Dimensional U-Convolutional Neural Network to achieve accurate segmentation of multimodal biomedical images, enhancing precision and comprehensiveness in identifying and analyzing structures across diverse imaging modalities. As a result of the enhancements, we propose a novel framework called Multi-Dimensional U-Convolutional Neural Network (MDU-CNN) as a potential successor to the U-Net framework. On a large set of multimodal medical images, we compared our proposed framework, MDU-CNN, to the classical U-Net. There have been small changes in the case of perfect images, and a huge improvement is obtained in the case of difficult images. We tested our model on five distinct datasets, each of which presented unique challenges, and found that it has obtained a better performance of 1.32%, 5.19%, 4.50%, 10.23% and 0.87%, respectively.

Abstract The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection.

Due to its potential use in evaluating mental health and enhancing patient care, emotion recognition using physiological signals, such as EEG, ECG, and EMG, has drawn a lot of attention in the healthcare industry. However, better neural network architecture and training parameter optimization is needed to achieve accurate and dependable emotion classification. This research aims to investigate the impact of various configurations of neural network architectures, proposed multimodal fusion network and training parameters on the classification performance of emotion recognition systems utilizing EEG, ECG, and EMG signals. Nine experiments were conducted, each representing a unique combination of hyperparameters, including EEG filters, kernel sizes, pooling sizes, ECG filters, EMG filters, LSTM units, dropout rates, epochs, and batch sizes. The experiments were designed to systematically explore the effects of these hyperparameters on classification accuracy, robustness, and computational efficiency. The experiments revealed that the choice of hyperparameters significantly influences the performance of emotion recognition systems. Proposed model achieved higher average accuracies, with Experiment 3 exhibiting the highest accuracy of 87.83%. However, the model also incurred higher computational costs in terms of training time and memory usage. Additionally, experiments with higher dropout rates demonstrated a slight decrease in accuracy compared to those with lower dropout rates, emphasizing the importance of balancing regularization and model complexity. The findings underscore the importance of fine-tuning hyperparameters and selecting appropriate neural network architectures to optimize the performance of emotion recognition systems. Advanced deep learning techniques and physiological signal processing, healthcare providers can develop robust and accurate emotion recognition systems to enhance patient care and support personalized interventions. Further research may focus on exploring novel architectures and refining training strategies to continue improving the efficacy and efficiency of AI-driven emotional intelligence solutions in healthcare delivery.

The growing reliance on distributed cloud technology in mobile healthcare applications has introduced critical challenges in ensuring secure and efficient access to Electronic Health Records (EHR). Traditional methods have prolonged authentication times and access delays, compromising both the efficiency and security of healthcare systems. To address these issues, this study proposes the Secure Healthcare Access Control System (SHACS), a robust framework specifically designed to enhance security and efficiency in healthcare environments. SHACS provides a sophisticated combination of role-based access control, attribute-based policies, and dynamic rules to streamline authentication processes and safeguard data access. SHACS architecture provides the central authority and system authorities, responsible for enforcing access control policies and verifying the authenticity of users requesting access to medical records. SHACS also integrates real-time anomaly detection capabilities, utilizing the MIMIC-III dataset to identify and respond to unusual access patterns, thereby mitigating potential security breaches. Following successful authentication, SHACS generates secure decryption tokens and keys, enabling swift and secure access to EHRs while continuously updating a dynamic access list to monitor and reduce access delays. Experimental results demonstrate that SHACS significantly improves system performance, reducing authentication times by 30% and access delays by 25% compared to traditional methods. For instance, SHACS decreased the average authentication time from 40 seconds to 28 seconds and enhanced system responsiveness, lowering average access delays from 15 seconds to 11 seconds. The implementation of SHACS underscores the importance of privacy-enhancing technologies in safeguarding medical records, ensuring that only authorized personnel access sensitive data. Through rigorous testing and analysis, SHACS proves its efficacy in strengthening the security posture of cloud-based healthcare systems, ultimately contributing to the quality and accessibility of remote healthcare services.

Abstract Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.

Abstract Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively.

Abstract The electroencephalogram (EEG) has emerged over the past few decades as one of the key tools used by clinicians to detect seizures and other neurological abnormalities of the human brain. The proper diagnosis of epilepsy is crucial due to its distinctive nature and the subsequent negative effects of epileptic seizures on patients. The classification of minimally pre-processed, raw multichannel EEG signal recordings is the foundation of this article’s unique method for identifying seizures in pre-adult patients. The new method makes use of the automatic feature learning capabilities of a three-dimensional deep convolution auto-encoder (3D-DCAE) associated with a neural network-based classifier to build an integrated framework that endures training in a supervised manner to attain the highest level of classification precision among brain state signals, both ictal and interictal. A pair of models were created and evaluated for testing and assessing our method, utilizing three distinct EEG data section lengths, and a tenfold cross-validation procedure. Based on five evaluation criteria, the labelled hybrid convolutional auto-encoder (LHCAE) model, which utilizes a classifier based on bidirectional long short-term memory (Bi-LSTM) and an EEG segment length of 4 s, had the best efficiency. This proposed model has 99.08 ± 0.54% accuracy, 99.21 ± 0.50% sensitivity, 99.11 ± 0.57% specificity, 99.09 ± 0.55% precision, and an F1-score of 99.16 ± 0.58%, according to the publicly available Children’s Hospital Boston (CHB) dataset. Based on the obtained outcomes, the proposed seizure classification model outperforms the other state-of-the-art method’s performance in the same dataset.

Abstract An abnormal growth or fatty mass of cells in the brain is called a tumor. They can be either healthy (normal) or become cancerous, depending on the structure of their cells. This can result in increased pressure within the cranium, potentially causing damage to the brain or even death. As a result, diagnostic procedures such as computed tomography, magnetic resonance imaging, and positron emission tomography, as well as blood and urine tests, are used to identify brain tumors. However, these methods can be labor-intensive and sometimes yield inaccurate results. Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive equipment, produce more accurate results, and are easy to set up. In this study, we propose a method based on transfer learning, utilizing the pre-trained VGG-19 model. This approach has been enhanced by applying a customized convolutional neural network framework and combining it with pre-processing methods, including normalization and data augmentation. For training and testing, our proposed model used 80% and 20% of the images from the dataset, respectively. Our proposed method achieved remarkable success, with an accuracy rate of 99.43%, a sensitivity of 98.73%, and a specificity of 97.21%. The dataset, sourced from Kaggle for training purposes, consists of 407 images, including 257 depicting brain tumors and 150 without tumors. These models could be utilized to develop clinically useful solutions for identifying brain tumors in CT images based on these outcomes.

Abstract COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets.

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by motor deficits, including tremor, rigidity, bradykinesia, and postural instability. According to the World Health Organization, about 1 % of the global population has been diagnosed with PD, and this figure is expected to double by 2040. Early and accurate diagnosis of PD is critical to slowing down the progression of the disease and reducing long-term disability. Due to the complexity of the disease, it is difficult to accurately diagnose it using traditional clinical tests. Therefore, it has become necessary to develop intelligent diagnostic models that can accurately detect PD. This article introduces a novel hybrid approach for accurate prediction of PD using an ANFIS with two optimizers, namely Adam and PSO. ANFIS is a type of fuzzy logic system used for nonlinear function approximation and classification, while Adam optimizer has the ability to adaptively adjust the learning rate of each individual parameter in an ANFIS at each training step, which helps the model find a better solution more quickly. PSO is a metaheuristic approach inspired by the behavior of social animals such as birds. Combining these two methods has potential to provide improved accuracy and robustness in PD diagnosis compared to existing methods. The proposed method utilized the advantages of both optimization techniques and applied them on the developed ANFIS model to maximize its prediction accuracy. This system was developed by using an open access clinical and demographic data. The chosen parameters for the ANFIS were selected through a comparative experimental analysis to optimize the model considering the number of fuzzy membership functions, number of epochs of ANFIS, and number of particles of PSO. The performance of the two ANFIS models: ANFIS (Adam) and ANFIS (PSO) focusing at ANFIS parameters and various evaluation metrics are further analyzed in detail and presented, The experimental results showed that the proposed ANFIS (PSO) shows better results in terms of loss and precision, whereas, the ANFIS (Adam) showed the better results in terms of accuracy, f1-score and recall. Thus, this adaptive neural-fuzzy algorithm provides a promising strategy for the diagnosis of PD, and show that the proposed models show their suitability for many other practical applications.

Abstract Heart disease is a significant global cause of mortality, and predicting it through clinical data analysis poses challenges. Machine learning (ML) has emerged as a valuable tool for diagnosing and predicting heart disease by analyzing healthcare data. Previous studies have extensively employed ML techniques in medical research for heart disease prediction. In this study, eight ML classifiers were utilized to identify crucial features that enhance the accuracy of heart disease prediction. Various combinations of features and well-known classification algorithms were employed to develop the prediction model. Neural network models, such as Naïve Bayes and Radial Basis Functions, were implemented, achieving accuracies of 94.78% and 90.78% respectively in heart disease prediction. Among the state-of-the-art methods for cardiovascular problem prediction, Learning Vector Quantization exhibited the highest accuracy rate of 98.7%. The motivation behind predicting Cardiovascular Heart Disease lies in its potential to save lives, improves health outcomes, and allocates healthcare resources efficiently. The key contributions encompass early intervention, personalized medicine, technological advancements, the impact on public health, and ongoing research, all of which collectively work toward reducing the burden of CHD on both individual patients and society as a whole.

In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images. The method combines semantic and spatial data using cGAN framework to produce realistic urban scenes while maintaining crucial details. To assess the performance of the proposed method, extensive experiments are performed on benchmark datasets, the ISPRS Potsdam and Vaihingen datasets. Intersection over Union (IoU) and Pixel Accuracy are two quantitative metrics used to evaluate the segmentation accuracy of the produced maps. The proposed method outperforms traditional methods with an IoU of 87% and a Pixel Accuracy of 93%. The experimental findings show that the suggested cGAN-based method performs better than traditional techniques, attaining better segmentation accuracy and generating better urban maps with finely detailed information. The suggested approach provides a framework for resolving the image-to-image translation difficulties in urban scene segmentation, demonstrating the potential of cGANs for producing excellent urban maps from satellite data.

The proposed hybrid CNN-BiLSTM architecture aims to address the challenge of detecting epileptic seizures systematically from EEG signal analysis. The system consists of several stages, including preprocessing, feature extraction using multi-dimensional CNN, temporal feature processing using BiLSTM, and classification using fully connected layers. The first stage involves preprocessing and normalization of the raw EEG signal to prepare it for further analysis. This step helps in removing noise and standardizing the input for subsequent processing. Next, a multi-dimensional CNN is employed to effectively extract features from the preprocessed EEG sequence data. CNNs are known for their ability to capture spatial features, and in this case, they are utilized to extract relevant features from the EEG data. After the feature extraction stage, the BiLSTM component of the architecture is utilized to process the extracted features and capture temporal dependencies. BiLSTMs are well-suited for sequence modeling tasks and can effectively capture long-range dependencies in the data. By incorporating BiLSTM, the architecture aims to capture important temporal patterns related to epileptic seizures. Finally, the temporal feature values are fed into fully connected layers for classification. The system is designed to detect epileptic seizures and classify them into specific types using a 10-class classification approach. The proposed system reports high detection accuracy with an overall accuracy of 99.53% and an accuracy of 82.95% on the binary classification task. These results suggest that the system performs well in accurately identifying epileptic seizures from EEG signals. Furthermore, the proposed system demonstrates superior performance compared to other existing techniques such as K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) in terms of accuracy. It is important to note that the evaluation and comparison of the proposed system were performed on a publicly available epileptic seizures image database. Overall, the proposed hybrid CNN-BiLSTM architecture shows potential in enhancing the detection and classification of epileptic seizures from EEG signals, potentially improving the efficiency and accuracy of diagnosis and treatment in the early stages of these disorders.

Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational methodology unfolds across four strata: the data origin layer, data acquisition layer, data classification layer, and data optimization layer. Central to this endeavor are multi-objective optimal datasets (MooM), characterized by attribute-driven feature cartography and cluster categorization through the conduit of federated learning models. The orchestration of feature synchronization and parameter extraction transpires across multiple tiers of neural networking, culminating in the provisioning of a steadfast remedy through dataset standardization and labeling. The empirical findings reflect the efficacy of the proposed technique, boasting an impressive 97.34% accuracy rate in the disentanglement and clustering of telemedicine data, facilitated by the operational servers within the ambit of the federated model.

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies.

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.

To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

Pollution is the primary ecological threat to nature and it also raises the temperature of the Earth. The primary cause of air pollution is exhaust emissions from the combustion chamber. This study aims to focus on controlling emissions using an aqueous nanoemulsion method and to analyze positive vibrations for the reduction of pollutants in diesel engines. In this study 3% NWED (nano-based water emulsion diesel), 7% NWED, and 12% NWED emulsion blended diesel were mixed with Tween-20 as a solvent. In addition, this work was accomplished with a multiuser remote control system for the continuous monitoring of emissions with IoT technology applied to diesel engines. Experimental results have been measured with an IoT kit and five gas analyzers and reported that emission of oxides of nitrogen and hydrocarbons can be reduced by using a 7% NWED blend when compared with diesel. In addition, the experimental result also shows that the brake-specific fuel consumption is reduced with an improvement in thermal efficiency at 7% NWED when compared with other blends.

Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system’s performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods.

Wireless sensor networks (WSNs) are used for recording the information from the physical surroundings and transmitting the gathered records to a principal location via extensively disbursed sensor nodes. The proliferation of sensor devices and advances in size, deployment costs, and user-friendly interfaces have spawned numerous WSN applications. The WSN should use a routing protocol to send information to the sink over a low-cost link. One of the foremost vital problems is the restricted energy of the sensing element and, therefore, the high energy is consumed throughout the time. An energy-efficient routing may increase the lifetime by consuming less energy. Taking this into consideration, this paper provides a multi-criteria clustering and optimal bio-inspired routing algorithmic rule to reinforce network lifetime, to increase the operational time of WSN-based applications and make robust clusters. Clustering is a good methodology of information aggregation that increases the lifetime by group formation. Multi-criteria clustering is used to select the optimal cluster head (CH). After proper selection of the CH, moth flame and salp swarm optimization algorithms are combined to analyze the quality route for transmitting information from the CH to the sink and expand the steadiness of the network. The proposed method is analyzed and contrasted with previous techniques, with parameters such as energy consumption, throughput, end-to-end delay, latency, lifetime, and packet delivery rate. Consumption of energy is minimized by up to 18.6% and network life is increased up to 6% longer compared to other routing protocols.

In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.

Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.