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Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians. [ABSTRACT FROM AUTHOR]

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm. [ABSTRACT FROM AUTHOR]

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Precise neurosurgical guidance is critical for successful brain surgeries and plays a vital role in all phases of image-guided neurosurgery (IGN). Neuronavigation software enables real-time tracking of surgical tools, ensuring their presentation with high precision in relation to a virtual patient model. Therefore, this work focuses on the development of a novel multimodal IGN system, leveraging deep learning and explainable AI to enhance brain tumor surgery outcomes. The study establishes the clinical and technical requirements of the system for brain tumor surgeries. NeuroIGN adopts a modular architecture, including brain tumor segmentation, patient registration, and explainable output prediction, and integrates open-source packages into an interactive neuronavigational display. The NeuroIGN system components underwent validation and evaluation in both laboratory and simulated operating room (OR) settings. Experimental results demonstrated its accuracy in tumor segmentation and the success of ExplainAI in increasing the trust of medical professionals in deep learning. The proposed system was successfully assembled and set up within 11 min in a pre-clinical OR setting with a tracking accuracy of 0.5 (± 0.1) mm. NeuroIGN was also evaluated as highly useful, with a high frame rate (19 FPS) and real-time ultrasound imaging capabilities. In conclusion, this paper describes not only the development of an open-source multimodal IGN system but also demonstrates the innovative application of deep learning and explainable AI algorithms in enhancing neuronavigation for brain tumor surgeries. By seamlessly integrating pre- and intra-operative patient image data with cutting-edge interventional devices, our experiments underscore the potential for deep learning models to improve the surgical treatment of brain tumors and long-term post-operative outcomes. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach—"fusion of end-to-end and transfer learning"—to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings. [ABSTRACT FROM AUTHOR]

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper. [ABSTRACT FROM AUTHOR]

Images are powerful tools with which to convey human emotions, with different images stimulating diverse emotions. Numerous factors affect the emotions stimulated by the image, and many researchers have previously focused on low-level features such as color, texture and so on. Inspired by the successful use of deep convolutional neural networks (CNN) in the visual recognition field, we used a data augmentation method for small data sets to gain the sufficient number of the training dataset. In this paper, we use low-level features (color and texture features) of the image to assist the extraction of advanced features (image object category features and deep emotion features of images), which are automatically learned by deep networks, to obtain more effective image sentiment features. Then, we use the stack sparse auto-encoding network to recognize the emotions evoked by the image. Finally, high-level semantic descriptive phrases including image emotions and objects are output. Our experiments are carried out on the IAPS and GAPED data sets of the dimension space and the artphoto data set of the discrete space. Compared with the traditional manual extraction methods and other existing models, our method is superior to in terms of test performance. [ABSTRACT FROM AUTHOR]

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods. [ABSTRACT FROM AUTHOR]

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce. In addition, existing SLO image segmentation methods still have difficulty in balancing accuracy and model parameters. This paper proposes a SLO image segmentation model based on lightweight U-Net architecture called MBRNet, which solves the problems in the current research through Multi-scale Bottleneck Residual (MBR) module and attention mechanism. Concretely speaking, the MBR module expands the receptive field of the model at a relatively low computational cost and retains more detailed information. Attention Gate (AG) module alleviates the disturbance of noise so that the network can concentrate on vascular characteristics. Experimental results on two public SLO datasets demonstrate that by comparison to existing methods, the MBRNet has better segmentation performance with relatively few parameters. [ABSTRACT FROM AUTHOR]

Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world, causing then the 2019-20 coronavirus pandemic. In many cases, this disease causes pneumonia. Since pulmonary infections can be observed through radiography images, this paper investigates deep learning methods for automatically analyzing query chest X-ray images with the hope to bring precision tools to health professionals towards screening the COVID-19 and diagnosing confirmed patients. In this context, training datasets, deep learning architectures and analysis strategies have been experimented from publicly open sets of chest X-ray images. Tailored deep learning models are proposed to detect pneumonia infection cases, notably viral cases. It is assumed that viral pneumonia cases detected during an epidemic COVID-19 context have a high probability to presume COVID-19 infections. Moreover, easy-to-apply health indicators are proposed for estimating infection status and predicting patient status from the detected pneumonia cases. Experimental results show possibilities of training deep learning models over publicly open sets of chest X-ray images towards screening viral pneumonia. Chest X-ray test images of COVID-19 infected patients are successfully diagnosed through detection models retained for their performances. The efficiency of proposed health indicators is highlighted through simulated scenarios of patients presenting infections and health problems by combining real and synthetic health data.

Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope. [ABSTRACT FROM AUTHOR]

In the past decades, the incidence rate of cancer has steadily risen. Although advances in early and accurate detection have increased cancer survival chances, these patients must cope with physical and psychological sequelae. The lack of personalized support and assistance after discharge may lead to a rapid diminution of their physical abilities, cognitive impairment, and reduced quality of life. This paper proposes a personalized support system for cancer survivors based on a cohort and trajectory analysis (CTA) module integrated within an agent-based personalized chatbot named EREBOTS. The CTA module relies on survival estimation models, machine learning, and deep learning techniques. It provides clinicians with supporting evidence for choosing a personalized treatment, while allowing patients to benefit from tailored suggestions adapted to their conditions and trajectories. The development of the CTA within the EREBOTS framework enables to effectively evaluate the significance of prognostic variables, detect patient's high-risk markers, and support treatment decisions. [ABSTRACT FROM AUTHOR]

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future. [ABSTRACT FROM AUTHOR]

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases. [ABSTRACT FROM AUTHOR]

Electronic Medical Records (EMRs) are written in an unstructured way, often using natural language. Information Extraction (IE) may be used for acquiring knowledge from such texts, including the automatic recognition of meaningful entities, through models for Named Entity Recognition (NER). However, while most work on the previous was made for English, this experience aimed at testing different methods in Portuguese text, more precisely, on the domain of Neurology, and take some conclusions. This paper comprised the comparison between Conditional Random Fields (CRF), bidirectional Long Short-term Memory - Conditional Random Fields (BiLSTM-CRF) and a BiLSTM-CRF with residual learning connections, using not only Portuguese texts from medical journals but also texts from the Coimbra Hospital and Universitary Centre (CHUC) Neurology Service. Furthermore, the performances of BiLSTM-CRF models using word embeddings (WEs) trained with clinical text and WEs trained with general language texts were compared. Deep learning models achieved F1-Scores of nearly 83% and 75%, respectively for relaxed and strict evaluation, on texts extracted from the medical journal. For texts collected from the Hospital, the same achieved F1-Scores of nearly 71% and 62%. This work concludes that deep learning models outperform the shallow learning models and that in-domain WEs get better results than general language WEs, even when the latter are trained with much more text than the former. Furthermore, the results show that it is possible to extract information from Hospital clinical texts with models trained with clinical cases extracted from medical journals, and thus openly available. Nevertheless, such results still require a healthcare technician to check if the information is well extracted.

This paper dispenses an exhaustive review on deep learning techniques used in the prognosis of eight different neuropsychiatric and neurological disorders such as stroke, alzheimer, parkinson's, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis. These diseases are critical, life-threatening and in most of the cases may lead to other precarious human disorders. Deep learning techniques are emerging soft computing technique which has been lucratively used to unravel different real-life problems such as pattern recognition (Face, Emotion, and Speech), traffic management, drug discovery, disease diagnosis, and network intrusion detection. This study confers the discipline, frameworks, and methodologies used by different deep learning techniques to diagnose different human neurological disorders. Here, one hundred and thirty-six different articles related to neurological and neuropsychiatric disorders diagnosed using different deep learning techniques are studied. The morbidity and mortality rate of major neuropsychiatric and neurological disorders has also been delineated. The performance and publication trend of different deep learning techniques employed in the investigation of these diseases has been examined and analyzed. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. The research implication, challenges and the future directions related to the study have also been highlighted. Eventually, the research breaches are identified and it is witnessed that there is more scope in the diagnosis of migraine, cerebral palsy and stroke using different deep learning models. Likewise, there is a potential opportunity to use and explore the performance of Restricted Boltzmann Machine, Deep Boltzmann Machine and Deep Belief Network for diagnosis of different human neuropsychiatric and neurological disorders.

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.

How to differentiate thyroid cancer nodules from a large number of benign nodules is always a challenging subject for clinicians. This paper proposes a novel Sal-deel network model to achieve the classification and diagnosis of thyroid cancer, which can simulate visual attention mechanism. The Sal-deep network introduces saliency map as an additional information on the deep residual network, which selectively enhances the feature extracted from different regions according to the mask map. Sal-deep network can work effectively for the benchmark networks with different data sets and different structures, and it is a universal network model. Sal-deep network increases the complexity of the network, but improves the efficiency of the network. A large number of qualitative and quantitative experiments show that our improved network is superior to other existing deep models in terms of classification accuracy rate and Recall, which is suitable for clinical application.

In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the complex models such as deep learning achieve better performance than the simple ones due to the nonlinear hypotheses of the real world clinical data. However,complex model as a black box, which ignores why it make a certain decision, causes the model distrust from clinicians. To solve these issues, an explainable artificial intelligence (XAI) framework is proposed in this paper to give the global and local interpretation of auxiliary diagnosis of hepatitis while retaining the good prediction performance. First, a public hepatitis classification benchmark from UCI is used to test the feasibility of the framework. Then, the transparent and black-box machine learning models are both employed to forecast the hepatitis deterioration. The transparent models such as logistic regression (LR), decision tree (DT)and k-nearest neighbor (KNN) are picked. While the black-box model such as the eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), random forests (RF) are selected. Finally, the SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME) and Partial Dependence Plots (PDP) are utilized to improve the model interpretation of liver disease. The experimental results show that the complex models outperform the simple ones. The developed RF achieves the highest accuracy (91.9%) among all the models. The proposed framework combining the global and local interpretable methods improves the transparency of complex models, and gets insight into the judgments from the complex models, thereby guiding the treatment strategy and improving the prognosis of hepatitis patients. In addition, the proposed framework could also assist the clinical data scientists to design a more appropriate structure of CAD. [ABSTRACT FROM AUTHOR]

Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification.

The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients. Usually, the brain tumor is detected using MRI scanning method. However, the radiologists are not providing the effective tumor segmentation in MRI image due to the irregular shape of tumors and position of tumor in the brain. Accurate brain tumor segmentation is needed to locate the tumor and it is used to give the correct treatment for a patient and it provides the key to the doctor who must execute the surgery for patient. In this paper, a novel deep learning algorithm (kernel based CNN) with M-SVM is presented to segment the tumor automatically and efficiently. This presented work contains several steps that are preprocessing, feature extraction, image classification and tumor segmentation of brain. The MRI image is smoothed and enhanced by Laplacian of Gaussian filtering method (LoG) with Contrast Limited Adaptive Histrogram Equalization (CLAHE) and feature can be extracted from it based on tumor shape position, shape and surface features in brain. Consequently, the image classification is done using M-SVM depending on the selected features. From MRI image, the tumor is segmented with help of kernel based CNN method.. Experimental results of proposed method can show that this presented technique can executes brain tumor segmentation accurately reaching almost 84% in evaluation with existing algorithms.

Diabetes, a metabolic disorder due to high blood glycemic index in the human body. The glycemic index varies in the human of improper diet and eating pattern such as junk foods, variation in the quantity of food, swallowing of food without chewing and stress. However, the diagnose of increase or decrease in the glycemic index is a challenging task. Similarly, the regulation of glycemic index without regular exercise is a major problem in day to day life. In this paper, we propose a novel SCS method to regulate glycemic index without exercise through changing the eating method. The proposed SCS eating method consists of Size of the food, Chewing style and Swallow time (SCS) of the food to regulate glycemic index. Furthermore, the proposed SCS method evaluate and validate through the acoustic signal acquired and processed with deep learning algorithm to analyze the chewing pattern of food to formulate a standard procedure for eating style and to reduce the glycemic level. The validation of diabetes done by measurement of blood glycemic through AccuChek Instant S Glucometer. Furthermore, the SCS method of eating style from 50 diabetes persons reduces the blood glucose level drastically by 85% after following the proposed method of eating style.

Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes. [ABSTRACT FROM AUTHOR]

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC. [ABSTRACT FROM AUTHOR]

Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies. [ABSTRACT FROM AUTHOR]

Existing brain region segmentation algorithms based on deep convolutional neural networks (CNN) are inefficient for object boundary segmentation. In order to enhance the segmentation accuracy of brain tissue, this paper proposed an object region segmentation algorithm that combines pixel-level information and semantic information. Firstly, we extract semantic information by CNN with the attention module and get the coarse segmentation results through a specific pixel-level classifier. Then, we exploit conditional random fields to model the relationship between the underlying pixels so as to get local features. Finally, the semantic information and the local pixel-level information are respectively used as the unary potential and the binary potential of the Gibbs distribution, and the combination of both can obtain the fine region segmentation algorithm based on the fusion of pixel-level information and the semantic information. A large number of qualitative and quantitative test results show that our proposed algorithm has higher precision than the existing state-of-the-art deep feature models, which can better solve the problem of rough edge segmentation and produce good 3D visualization effect. [ABSTRACT FROM AUTHOR]

The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techniques, systems are now well equipped in diagnosing many health problems. Utilizing the intensity of substantial historical information in electronic health record (EHR), we built up, a conventional predictive temporal model utilizing recurrent neural systems (RNN) like LSTM and connected to longitudinal time stepped EHR. Experience records were contribution to RNN to anticipate the analysis and prescription classes for a resulting visit during heart disappointment (e.g. diagnosis codes, drug codes or method codes). In this paper, we also investigated whether use of deep learning to model temporal relations among events in electronic health records (EHRs) would enhance the model performance in predicting initial diagnosis of heart failure (HF) compared to some of the traditional methods that disregard temporality. By examining these time stamped EHRs, we could recognize the associations between various diagnosis occasions and finally predicate when a patient is being analyzed for a disease. In any case, it is hard to access the current EHR data straightforwardly, since almost all data are sparse and not standardized. Along these lines, we proposed a robust model for prediction of heart failure. The fundamental commitment of this paper is to predict the failure of heart by means of a neural network model based on patient's electronic medicinal information. In order to, demonstrate the diagnosis events and prediction of heart failure, we used the medical concept vectors and the essential standards of a long short-term memory (LSTM) deep network model. The proposed LSTM model uses SiLU and tanh as activation function in the hidden layers and Softmax in output layer in the network. Bridgeout is used as a regularization technique for weight optimization throughout the network. Assessments subject to the real-time data exhibit the favorable effectiveness and feasibility of recommended model in the risk of heart failure prediction. The results showed improved accuracy in heart failure detection and the model performance is compared using the existing deep learning models. Enhanced prior detection could expose novel chances for deferring or anticipating movement to analysis of heart failure and diminish cost. [ABSTRACT FROM AUTHOR]

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense micro-block difference feature (DMDF) into a unified framework so as to obtain segmentation results with appearance and spatial consistency. Firstly, we propose a local feature to describe the rotation invariant property of the texture. In order to deal with the change of rotation and scale in texture image, Fisher vector encoding method is used to analyze the texture feature, which can combine with the scale information without increasing the dimension of the local feature. The obtained local features have strong robustness to rotation and gray intensity variation. Then, the non-quantifiable local feature is fused to the FCNN to perform fine boundary segmentation. Since brain tumors occupy a small portion of the image, deconvolutional layers are designed with skip connections to obtain a high quality feature map. Compared with the traditional MRI brain tumor segmentation methods, the experimental results show that the segmentation accuracy and stability has been greatly improved. Average Dice index can be up to 90.98%. And the proposed method has very high real-time performance, where brain tumor image can segment within 1 s. [ABSTRACT FROM AUTHOR]