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Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice. [ABSTRACT FROM AUTHOR]

Skin cancer is a significant global health concern, and early detection is crucial for successful treatment outcomes. Traditional diagnostic methods require expensive tools, such as a dermatoscope, which may not be available at all facilities. Artificial intelligence (AI) is one potential solution, which could provide accurate analyses of regular photos and determine whether any skin lesion can be found. The aim of this study is to develop AI-based models for skin cancer detection through the help of sophisticated deep learning (DL) algorithms. For evaluated models, including Convolutional Neural Network (CNN), Xception, InceptionV3, NASNetMobile, and VGG16. Note that their comparison metrics consist of accuracy, precision, recall values, F1 score, AUC on ROC curve, and specificity. The CNN model performed best, where it gave an accuracy of 97% and AUC 0.9962. The Xception model, with an accuracy of 95% and AUC of 0.9800, came next. Our InceptionV3 model played hard with an accuracy of 93% and AUC is 0.9819. Other models such as NASNetMobile and VGG16 showed less performance compared to the above model as follows with accuracy: 69%, AUC: 0.8228, and accuracy of 77%, AUC: 0.8057 correspondingly. Our study shows the promise of AI for increasing both the accuracy and availability of skin cancer diagnosis, opening up a much-needed addition in the fight to lower treatment-critical oncologic morbidity and mortality. Our work intends to create a superior model for skin cancer detection and improve medical image analysis and diagnostic tools. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices. [ABSTRACT FROM AUTHOR]

Skin cancer poses a significant health hazard, necessitating the utilization of advanced diagnostic methodologies to facilitate timely detection, owing to its escalating prevalence in recent years. This paper proposes a novel approach to tackle the issue by introducing a method for detecting skin cancer that uses MixNets to enhance diagnosis and leverages mobile network-based transfer learning. Skin cancer has diverse forms, each distinguishable by its structural attributes, morphological characteristics, texture, and coloration. The pressing demand for accurate and efficient diagnostic instruments has spurred the investigation of novel techniques. The present study utilizes the ISIC dataset, comprising a validation set of 660 images and a training set of 2637 images. Moreover, the research employs a combination of MixNets and mobile network-based transfer learning as its chosen approach. Transfer learning is a technique that leverages preexisting models to enhance the diagnostic capabilities of the proposed system. Integrating MobileNet and MixNets allows for utilizing their respective functionalities, resulting in a dual-model methodology that enhances the comprehensiveness of skin cancer diagnosis. The results demonstrate impressive performance metrics, with MobileNet and MixNets models, and the proposed approach achieves an outstanding accuracy rate of 99.58%. The above findings underscore the efficacy of the dual-model method in effectively discerning between benign and malignant skin lesions. Moreover, the present study aims to examine the potential integration of emerging technologies to enhance the accuracy and practicality of diagnostics within real-world healthcare settings. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning–based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature. [ABSTRACT FROM AUTHOR]

Skin cancer is considered as one of the most serious types of cancer that leads to death worldwide. The number of deaths that caused by skin cancer can be reduced if it is diagnosed at early stages. Skin cancer is usually diagnosed using visual inspection, but it is less accurate. Using deep learning-based methods have been proposed to assist the doctors to diagnose the skin cancers at early accurately. The investigation was achieved on 3600 images collected from kaggle. Two deep learning algorithms used in the study: Vgg-19 and Alexnet to extract the layers 6 and 7. Then, the two layers will be merged to generate the statistical operations like: median, lowest, highest and joined of two layers. The following classifiers were used in the investigation: K-Nearest Neighbor, Random Forest, Naïve Bayes and Decision Tree. However, the study considered the following measures: accuracy, precision, recall, and F-Measure. Three training datasets sizes will be used to investigate the influence on classification accuracy. The results of all datasets were slightly similar. This approves that the extracted features and the statistical operations have an influence on the classification accuracy. The results show that Alexnet performs high accuracy and consumes less time that required for training the model compared to vgg-19. The results of classifiers showed that Random Forest scored high classification accuracy (85.6) compared to other ML classifiers. [ABSTRACT FROM AUTHOR]

A large portion of the human body is covered by skin, which shields the internal organs from unsafe elements such as heat, dust, ultraviolet rays, tainted water, and others. This is the reason for the various skin problems, such as cancer, rosacea, moles, and eczema. In this research, an optimized deep learning model is developed for skin lesion segmentation and cancer detection. At first, an input skin image is acquired from the dataset which is fed into image pre-processing using Bilateral Filter. The pre-processed image is next subjected to skin lesion segmentation using U-Net. Furthermore, image augmentation is carried out to enlarge size of the segmented image. Additionally, feature extraction is carried out to extract the important features, such as statistical features, Haralick texture features, shape local binary texture features, convolutional neural network features, Pyramid Histogram of Oriented Gradients, and Local Ternary pattern. The mean, variance, and kurtosis skewness are among the statistical properties present here. The contrast, homogeneity, correlation, and Angular Second Moment characteristics are the Haralick texture. Finally, skin cancer detection is accomplished using LeNet, which is trained by proposed Fire Gannet Optimization (FGO). Here, the FGO is newly devised by integration of Fire Hawk Optimization and Gannet optimization algorithm. The developed approach is proven to have a high accuracy, TPR, TNR, F1-score, and Matthews correlation coefficient with respective values of 91.4%, 93.9%, 89.9%, 90.7%, and 87.1% for 90% of training data. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and different feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%. [ABSTRACT FROM AUTHOR]

Skin cancer is a significant global health issue, with melanoma, basal cell carcinoma, and actinic keratosis being the most common types. Early and accurate detection is critical to improve survival rates and treatment outcomes. This study evaluates the performance of Convolutional Neural Networks (CNN) and ResNet50 in classifying segmented images of skin lesions. The dataset, sourced from Kaggle, was pre-processed using U-Net for lesion segmentation to enhance the quality of input data. Both models were trained and evaluated using accuracy, precision, recall, and F1-score metrics. The CNN model demonstrated a balanced performance across classes, with a weighted F1-score of 47%, but suffered from overfitting, as indicated by the divergence between training and validation losses. ResNet50 achieved better recall for basal cell carcinoma (100%) but failed to classify actinic keratosis and melanoma, resulting in a macro F1-score of 23%. The findings reveal that U-Net segmentation improved classification focus but was insufficient to address dataset imbalance and model-specific limitations. This study highlights the challenges of skin cancer classification using deep learning and underscores the importance of addressing data imbalance and overfitting. Future research should explore advanced techniques, such as ensemble methods, data augmentation, and transfer learning, to improve the generalization and clinical applicability of these models. The proposed framework serves as a foundation for further investigation into automated skin cancer detection systems.

Skin cancer is a dreadful disease, which is mainly caused due to the heavy exposure of the human body to the ultraviolet rays emitted from the sun. Although the mortality rate is very high, the survival rate is found to be superior when it is detected at its early stage. In this research, Chronological Dingo Optimizer (CDO)-based Deep Maxout Network (DMN) is developed for the skin cancer detection. Here, initially the images are pre-processed and then, the skin lesion segmentation is accomplished effectively by employing Double U-Net. Then, data augmentation is performed and the detection process is carried out by employing DMN, where the network is optimally fine-tuned utilizing designed CDO. The CDO is the integration of chronological concept with Dingo Optimizer (DOX). The proposed scheme shows better outcomes with superior sensitivity of 0.959, F-measure of 0.908, accuracy of 0.923 and specificity of 0.837. [ABSTRACT FROM AUTHOR]

Cutaneous melanoma is the most lethal skin cancer and noninvasively distinguishing it from benign tumor is a major challenge. Raman spectroscopic measurements were conducted on 65 suspected melanocytic lesions and surrounding healthy skin from 47 patients. Compared to the spectra of healthy skin, spectra of melanocytic lesions exhibited lower intensities in carotenoid bands and higher intensities in lipid and melanin bands, suggesting similar variations in the content of these components. Distinct variations were observed among the autofluorescence intensities of healthy skin, benign nevi and malignant melanoma. By incorporating autofluorescence information, the classification accuracy of the support vector machine for spectra of healthy skin, nevi, and melanoma reached 90.2%, surpassing the 87.9% accuracy achieved without autofluorescence, with this difference being statistically significant. These findings indicate the diagnostic value of autofluorescence intensity, which reflect differences in fluorophore content, chemical composition, and structure among healthy skin, nevi, and melanoma. [ABSTRACT FROM AUTHOR]

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909 ±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901 ±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy. [ABSTRACT FROM AUTHOR]

The cancer of the skin is a frequent type of cancer, and the chance of survival rises with early identification. One of the deadliest forms of cancer and a major global cause of death is skin cancer. Early detection of skin cancer can help lower the death toll. The most popular, albeit less accurate, technique for diagnosing cancer of the skin is visual examination. There have been suggestions for deep learning-based techniques to help physicians diagnose skin malignancies accurately and early. Early diagnosis of skin cancer signs is imperative due to the disease's rising incidence, high death rate, and cost of care. Given the gravity of these problems, scientists have created a number of early skin cancer screening methods. To develop deep learning models that use the Mobile-net technique to classify skin cancer. Our approach involves utilizing the ISIC dataset, which has 2991 photos of each of the six different kinds of skin lesion cancer, to identify as well as diagnose cancer of the skin using the Mobile-net algorithm. We employ five distinct modules in our article to carry out our project: dataset collection, preprocessing, training and testing data splitting, deep learning algorithm model implementation, and classification and prediction as the last module. Deep learning and image processing concepts are used in the diagnosis process. Based on the findings, the suggested approach performed admirably for a range of skin disorders and also used the patient's metadata together with the disease image for the classification of skin. [ABSTRACT FROM AUTHOR]

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model's effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics. [ABSTRACT FROM AUTHOR]

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation. [ABSTRACT FROM AUTHOR]

Abstract--The number of fatalities resulting from skin cancer has significantly increased over the past few years. Early diagnosis is highly important for the quick treatment of skin cancer. Computer-based dermoscopy analysis methods provide considerable information about the lesions that can be helpful to skin experts in the early detection of skin lesions. These computer-based diagnostic systems require image-processing algorithms to provide mathematical explanations of suspicious areas. Convolutional Neural Network (CNN) as one of the deep learning algorithms has high scalability in interaction with big data, and can automatically extract key image features for classification and segmentation of images. In this study, a hybrid model consisting of deep learning and machine learning method is proposed to classify different types of skin lesions. In this model, at first, an input image is pre-processed to remove the negative effect of Hairs on skin lesion detection and also to prepare it for applying to an efficient deep convolutional network employed as a feature extractor. Then Support Vector Machine (SVM) is utilized as a classifier to detect and classify different types of skin lesions.

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.

Skin cancer is the process of identifying and diagnosing, a disease in which abnormal skin cells grow and spread uncontrollably. An innovative deep learning-based skin cancer detection model is introduced in this research work. The proposed model is divided into five main phases: (a) Pre-Processing (b) Segmentation (c) Feature Extraction (d) 3-Tier Classification (e) post-processing. Initially, the collected raw image is pre-processed via contrast enhancement, decimal scaling, and augmentation methods. From the pre-processed image, the important feature is extracted by using the statistical features like mean, variance, and information gain. Then, from the extracted image, the region of interest ROI is identified via fuzzy assisted Kapur's multi-level thresholding. The optimal features are selected using the hybrid Self-Improved Chimp Optimization algorithm with Glow Swarm Optimization algorithm (HSCGS). The three-tier classification using the BERT based Transformer with HSCGS based Gated Recurred Unit (GRU), BiLSTM, and Graph Neural Network is projected for classification. The proposed model is implemented using the PYTHON platform. The findings are evaluated in terms of accuracy, sensitivity, precision, FPR, FNR, etc. using the present models. The proposed model has recorded the highest detection accuracy as 97% and highest MCC and NPV values. Proposed model has shown the best performance and has outperformed other models. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most deadly types of cancer that makes people less aware of the signs and symptoms. The skin cells are destroyed in people affected by skin cancer, a typical occurrence worldwide. As a result, accurate skin cancer detection earlier is crucial to reduce the risk of the disease spreading and raising the chances of survival. In recent years, medical applications have grown more interested in image processing and machine learning approaches. However, the model's performance is still vulnerable to image occlusions and inaccurate detection. Hence, a deep learning based skin cancer detection mechanism is introduced in this research. For the input image, the artefacts are removed using the pre-processing technique. Then, the essential region of interest (ROI) is segmented using the Dictionary Learning based Fuzzy C-Means (DicL-FCM) clustering technique. Then, the optimal best features are chosen using the proposed Chebyshev based Chaotic Genetic Optimization (C-CGO) algorithm from the extracted features. Finally, skin cancer detection is devised using the proposed Twin Attention based Convolutional Gated Recurrent Network (TA_CGRNet) model. The performance of the proposed Optimized TA_CGRNet is analyzed based on various assessment measures like Accuracy, Specificity, Precision, Recall, and F-Measure accomplished the values of 98.91%, 94.67%, 96.92%, 96.23%, and 96.54%, respectively. [ABSTRACT FROM AUTHOR]

Accurate classification of malignant and benign skin lesions is crucial in dermatology. In this novel research, we propose robust image analysis methodology for skin lesion classification that integrates color-based segmentation with luminosity analysis. Our approach is evaluated on a dataset of 400 skin images, with equal representation of malignant and benign samples. By computing mean color values for the Red Channel Color (RCC), Green Channel Color (GCC), and Blue Channel Color (BCC) in groups of 10 samples, we establish a classification range for precise diagnosis, this research introduces a novel dimension by harnessing the potential of the CIE Lab Color characteristics for skin lesion detection as the most reliable scale for distinguishing between benign and malignant samples. The smaller and more thought variety ranges saw in the glow examination improve difference and perceivability, consequently working with prevalent sore separation. By featuring the meaning of mean histograms for each variety channel, this complete exploration adds to propelling the area of dermatology and presents an imaginative methodology that holds guarantee for PC helped conclusion frameworks in skin malignant growth discovery. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most common types of cancer globally, by increasing occurrence rates each year. It is a predominant kind of cancer which rises from the uncontrolled growth of abnormal skin cells due to genetic mutations including various factors such as UV radiation, genetics and other factors. The death rate is decreased when skin cancer is detected at early stages. Therefore, this paper proposed a Convolutional Neural Network (CNN) with Coordinate Attention Module (CAM) for early detection of skin cancer. The data augmentation is utilized in this experiment for data pre-processing and fed into the Gray Level Co-occurrence Matrix (GLCM) based feature extraction technique. The Harris Hawk Optimization (HHO) is utilized for selecting features that have faster convergence and strong capability in local optima. The selected features are given as input to CNN with the CAM approach. This model is estimated on ISIC-2019 and ISIC-2020 datasets and attains better results using accuracy, precision, recall, specificity, and F1-score. The obtained result shows that the proposed model achieves better accuracy of 98.77% on the ISIC-2019 dataset and 99.51% on the ISIC-2020 dataset which ensures accurate detection compared to other existing methods like Inception-ResNet and Residual Deep Convolutional Neural Network (RDCNN). [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Summary: Exploring the associations between microRNAs (miRNAs) and diseases can identify potential disease features. Prediction of miRNA‐skin cancer associations has become an effective method for detecting skin cancer, as direct detection of skin cancer is challenging. However, traditional binary associations prediction methods overlook high‐order feature differences of miRNAs in different types of skin cancer. Although current tensor decomposition methods have addressed this, they assume a consistent composition standard of global tensor elements, ignoring differences in the composition standards of local sub‐tensors. This leads to poor prediction accuracy and comprehensiveness and fails to consider the credibility and diversity requirements of patients and doctors in practical applications. In this paper, we represent miRNA‐skin cancer associations as a tensor and employ tensor decomposition for tensor completion to achieve prediction purposes. First, we propose a credibility evaluation indicator and introduce four objective functions: accuracy, comprehensiveness, credibility, and diversity, to construct a many‐objective local tensor factorization model (MOLTF). Then, to avoid wrong individuals when solving this model with genetic algorithms, we propose a corrected single‐point crossover operator and a corrected multi‐point mutation operator. On the miRNA‐disease dataset HMDD v3.2, our algorithm improves accuracy by 14.5% compared to the recent baseline, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

Abstract--The number of fatalities resulting from skin cancer has significantly increased over the past few years. Early diagnosis is highly important for the quick treatment of skin cancer. Computer-based dermoscopy analysis methods provide considerable information about the lesions that can be helpful to skin exfats in the early detection of skin lesions. These computer-based diagnostic systems require image-processing algorithms to provide mathematical explanations of suspicious areas. Convolutional Neural Network (CNN) as one of the deep learning algorithms has high scalability in interaction with big data, and can automatically extract key image features for classification and segmentation of images. In this study, a hybrid model consisting of deep learning and machine learning method is proposed to classify different types of skin lesions. In this model, at first, an input image is pre-processed to remove the negative effect of Hairs on skin lesion detection and also to prepare it for applying to an efficient deep convolutional network employed as a feature extractor. Then Support Vector Machine (SVM) is utilized as a classifier to detect and classify different types of skin lesions. [ABSTRACT FROM AUTHOR]

Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays a crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer detection. In this paper, we enhanced the VGG19 pre-trained model with max pooling and dense layer for the prediction of skin cancer. Moreover, we also explored the pre-trained models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual Network version 2 (InceptionResNetV2), Dense Convolutional Network 201 (DenseNet201), Residual Network 50 (ResNet50), Inception version 3 (InceptionV3), For training, skin lesions dataset is used with malignant and benign cases. The models extract features and divide skin lesions into two categories: malignant and benign. The features are then fed into machine learning methods, including Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM), our results demonstrate that combining E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. Moreover, we have also compared the performance of baseline classifiers and pre-trained models with metrics (recall, F1 score, precision, sensitivity, and accuracy). The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. This research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments.

Malignant tissue in the skin is highly harmful. As melanoma is of identical look and lacks color variation, detection of skin cancer from dermatoscopic scans is a difficult task. The raw dataset is imbalanced that has been rarely studied. This is considered in this work to improve the accuracy level. The data have been studied and balanced using Synthetic Minority Oversampling Technique (SMOTE). The classifiers are considered as fuzzy-based classifiers that are statistical in nature and can pick the specific class, so that all the oversampled may not be useful except the informative samples. A combination of both homogeneous and heterogeneous ensemble learning termed a hybrid ensemble learning model is proposed. The homogeneity is formed by using two similar models, i.e., two adaptive resonance theory mapping (ARTMAP) models. One ARTMAP is trained with the raw imbalanced dataset, whereas the other one is trained with the balanced dataset. The heterogeneity is considered using fuzzy min–max (FMM) as the third base classifier. Its learning strategy is different from ARTMAP. Finally, the classification is performed using the rule-based neuro-Fuzzy classification (NEFCLASS) model. The open-source Kaggle HAM10000 skin dermatoscopic dataset is used for the training and detection of skin cancer. The proposed model provided 98.4% classification accuracy that competes with state-of-the-art models in the field of skin cancer detection. An improved form of ensemble learning is proved to be an efficient choice in the field of image processing. [ABSTRACT FROM AUTHOR]

Simple Summary: This research revolutionizes dermatological diagnostics by integrating artificial intelligence (AI) and hyperspectral imaging (HSI) to identify skin cancer lesions, particularly Mycosis fungoides (MF). It differentiates MF from conditions such as psoriasis and atopic dermatitis using a dataset of 1659 skin images. This study employs a novel AI algorithm alongside advanced techniques for precise lesion segmentation and classification, moving diagnosis from color to spectral analysis. This non-invasive and efficient method marks a significant advancement in the early and accurate detection of skin malignancies. The model's high performance is validated by its sensitivity, specificity, and accuracy, making it a vital tool in dermatology for identifying skin cancers and inflammatory conditions. This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions. [ABSTRACT FROM AUTHOR]

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model’s effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics.

Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient. [ABSTRACT FROM AUTHOR]