Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

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SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Mathematics, Journal Year: 2024, Volume and Issue: 12(7), P. 1030 - 1030

Published: March 29, 2024

The medical sciences are facing a major problem with the auto-detection of disease due to fast growth in population density. Intelligent systems assist professionals early detection and also help provide consistent treatment that reduces mortality rate. Skin cancer is considered be deadliest most severe kind cancer. Medical utilize dermoscopy images make manual diagnosis skin This method labor-intensive time-consuming demands considerable level expertise. Automated methods necessary for occurrence hair air bubbles dermoscopic affects research aims classify eight different types cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), benign (BKs). In this study, we propose SNC_Net, which integrates features derived from through deep learning (DL) models handcrafted (HC) feature extraction aim improving performance classifier. A convolutional neural network (CNN) employed classification. Dermoscopy publicly accessible ISIC 2019 dataset utilized train validate model. proposed model compared four baseline models, EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), ResNet-101 (B4), six state-of-the-art (SOTA) classifiers. With an accuracy 97.81%, precision 98.31%, recall 97.89%, F1 score 98.10%, outperformed SOTA classifiers as well models. Moreover, Ablation study performed on its performance. therefore assists dermatologists other detection.

Language: Английский

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Federated Learning Approach for Early Detection of Chest Lesion Caused by COVID-19 Infection Using Particle Swarm Optimization

Electronics, Journal Year: 2023, Volume and Issue: 12(3), P. 710 - 710

Published: Jan. 31, 2023

The chest lesion caused by COVID-19 infection pandemic is threatening the lives and well-being of people all over world. Artificial intelligence (AI)-based strategies are efficient methods for helping radiologists assessing vast number X-ray images, which may play a significant role in simplifying improving diagnosis infection. Machine learning (ML) deep (DL) such AI that have helped researchers predict cases. But ML DL face challenges like transmission delays, lack computing power, communication privacy concerns. Federated Learning (FL) new development makes it easier to collect, process, analyze large amounts multidimensional data. This could help solve been identified DL. However, FL algorithms send receive weights from client-side trained models, resulting overhead. To address this problem, we offer unified framework combining particle swarm optimization algorithm (PSO) speed up government’s response time outbreaks. Particle Swarm Optimization approach tested on image dataset (pneumonia) Kaggle’s repository. Our research shows proposed model works better when there an uneven amount data, has lower costs, therefore more network’s point view. results were validated; 96.15% prediction accuracy was achieved lesions dataset, 96.55% dataset. These can be used develop progressive early detection

Language: Английский

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A Novel Fusion Model of Hand-Crafted Features With Deep Convolutional Neural Networks for Classification of Several Chest Diseases Using X-Ray Images

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 39243 - 39268

Published: Jan. 1, 2023

With the continuing global pandemic of coronavirus (COVID-19) sickness, it is critical to seek diagnostic approaches that are both effective and rapid limit number people infected with severe acute respiratory syndrome 2 (SARS-CoV-2). The results recent research suggest radiological images include important information related COVID-19 other chest diseases. As a result, use deep learning (DL) assist in automated diagnosis diseases may prove useful as tool future. In this study, we propose novel fusion model hand-crafted features convolutional neural networks (DCNNs) for classifying ten different such COVID-19, lung cancer (LC), atelectasis (ATE), consolidation (COL), tuberculosis (TB), pneumothorax (PNET), edema (EDE), pneumonia (PNEU), pleural thickening (PLT), normal using X-rays (CXR). method has been suggested split down into three distinct parts. first step involves utilizing Info-MGAN network perform segmentation on raw CXR data construct second step, segmented fed pipeline extracts discriminatory by techniques SURF ORB, then these extracted fused trained DCNNs. At last, various machine (ML) models have used last layer DCNN classification Comparison made between performance proposed architectures classification, all which integrate DCNNs, key point extraction methods, ML models. We were able attain accuracy 98.20% testing VGG-19 softmax conjunction ORB technique. Screening ailments can be accomplished proposed. robustness was further confirmed statistical analyses datasets McNemar's ANOVA tests respectively.

Language: Английский

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Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0296352 - e0296352

Published: March 12, 2024

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by overlapping symptoms (such fever, cough, sore throat, etc.). Additionally, researchers make use X-rays (CXR), cough sounds, computed tomography (CT) scans diagnose disorders. The present study aims classify nine different disorders, including LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for classifications extracting features from images. Furthermore, proposed CNN employed several new approaches max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), multiple-way data generation (MWDG). scalogram method is utilized transform sounds coughing into visual representation. Before beginning model has been developed, SMOTE approach used calibrate CXR CT well sound images (CSI) CXR, scan, CSI training evaluating come 24 publicly available benchmark illness datasets. classification performance compared with seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, Inception-V3, in addition state-of-the-art (SOTA) classifiers. effectiveness further demonstrated results ablation experiments. was successful achieving an accuracy 99.01%, making it superior both SOTA As result, capable offering significant support radiologists other professionals.

Language: Английский

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Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(9), P. 99 - 99

Published: Aug. 28, 2024

Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated research applied to analysis, outlining technical contributions. We followed guidelines Okali and Schabram, a methodology, produce comprehensive summary discussion literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, Web Science. found total 433 papers selected 118 them further examination. The findings highlighted on applying neural network cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, urology. main challenges reported ability models adapt effectively real-world datasets privacy preservation. outlined two strategies address these challenges: non-independent identically distributed data privacy-enhancing methods. offers reference overview those already working field introduction new topic.

Language: Английский

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The Data Heterogeneity Issue Regarding COVID-19 Lung Imaging in Federated Learning: An Experimental Study

Big Data and Cognitive Computing, Journal Year: 2025, Volume and Issue: 9(1), P. 11 - 11

Published: Jan. 14, 2025

Federated learning (FL) has emerged as a transformative framework for collaborative learning, offering robust model training across institutions while ensuring data privacy. In the context of making COVID-19 diagnosis using lung imaging, FL enables to collaboratively train global without sharing sensitive patient data. A central manager aggregates local updates compute updates, secure and effective integration. The model’s generalization capability is evaluated centralized testing before dissemination participating nodes, where assessments facilitate personalized adaptations tailored diverse datasets. Addressing heterogeneity, critical challenge in medical essential improving both performance personalization systems. This study emphasizes importance recognizing real-world variability proposing solutions tackle non-independent non-identically distributed (non-IID) We investigate impact heterogeneity on imaging seven distinct settings. By comprehensively evaluating models metrics, we highlight challenges opportunities optimizing frameworks. findings provide valuable insights that can guide future research toward achieving balance between adaptation, ultimately enhancing diagnostic accuracy outcomes imaging.

Language: Английский

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Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest x-rays

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(23), P. 63017 - 63045

Published: Jan. 10, 2024

Language: Английский

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Heuristic-based image stitching algorithm with automation of parameters for smart solutions

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 241, P. 122792 - 122792

Published: Dec. 1, 2023

Language: Английский

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Recent methodological advances in federated learning for healthcare

Patterns, Journal Year: 2024, Volume and Issue: 5(6), P. 101006 - 101006

Published: June 1, 2024

For healthcare datasets, it is often impossible to combine data samples from multiple sites due ethical, privacy, or logistical concerns. Federated learning allows for the utilization of powerful machine algorithms without requiring pooling data. Healthcare have many simultaneous challenges, such as highly siloed data, class imbalance, missing distribution shifts, and non-standardized variables, that require new methodologies address. adds significant methodological complexity conventional centralized learning, distributed optimization, communication between nodes, aggregation models, redistribution models. In this systematic review, we consider all papers on Scopus published January 2015 February 2023 describe federated addressing challenges with We reviewed 89 meeting these criteria. Significant systemic issues were identified throughout literature, compromising reviewed. give detailed recommendations help improve methodology development in healthcare.

Language: Английский

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