Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans DOI Creative Commons
Hassaan Malik, Tayyaba Anees, Ahmad Naeem

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(2), P. 203 - 203

Published: Feb. 3, 2023

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed isolate COVID-19. When it comes determining identity COVID-19, one most significant obstacles that researchers overcome is propagation virus, in addition dearth trustworthy testing models. This problem continues difficult for clinicians deal with. The use AI image processing has made formerly insurmountable challenge finding COVID-19 situations more manageable. In real world, there handled about difficulties sharing data between hospitals while still honoring privacy concerns organizations. training global deep learning (DL) model, crucial handle fundamental such as user collaborative model development. For this study, novel framework designed compiles information from five different databases (several hospitals) edifies using blockchain-based federated (FL). validated through blockchain technology (BCT), FL trains on scale maintaining secrecy proposed divided into three parts. First, we provide method normalization can diversity collected sources several computed tomography (CT) scanners. Second, categorize patients, ensemble capsule network (CapsNet) with incremental extreme machines (IELMs). Thirdly, interactively BCT anonymity. Extensive tests employing chest CT scans comparison classification performance DL algorithms predicting protecting variety users, were undertaken. Our findings indicate improved effectiveness identifying patients achieved an accuracy 98.99%. Thus, our provides substantial aid medical practitioners their diagnosis

Language: Английский

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP DOI Creative Commons
Mohan Bhandari, Pratheepan Yogarajah, Muthu Subash Kavitha

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(5), P. 3125 - 3125

Published: Feb. 28, 2023

Kidney abnormality is one of the major concerns in modern society, and it affects millions people around world. To diagnose different abnormalities human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, used, which creates cross-sectional slices kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification segmentation purposes. However, has difficult clinicians interpret model’s specific decisions and, thus, creating “black box” system. Additionally, integrate complex internet-of-medical-things devices due demanding training parameters memory-resource cost. overcome these issues, this study proposed (1) lightweight customized convolutional neural network detect kidney cysts, stones, tumors (2) understandable AI Shapely values based on Shapley additive explanation predictive results local interpretable model-agnostic explanations illustrate model. The CNN model performed better than other state-of-the-art methods obtained an accuracy 99.52 ± 0.84% K = 10-fold stratified sampling. With improved interpretive power, work provides with conclusive results.

Language: Английский

Citations

36
Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets DOI Creative Commons
Xingsi Xue,

C. Seelammal,

Ghaida Muttashar Abdulsahib

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(3), P. 363 - 363

Published: March 16, 2023

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures chest X-rays computed tomography (CT) scans. Deep learning (DL) artificial intelligence (AI) are critical tools for early accurate detection of COVID-19. This research explores the different DL techniques identifying pneumonia on medical CT radiography images using ResNet152, VGG16, ResNet50, DenseNet121. The ResNet framework uses scan with accuracy precision. automates optimum model architecture training parameters. Transfer approaches also employed solve content gaps shorten duration. An upgraded VGG16 deep transfer is applied perform multi-class classification X-ray imaging tasks. Enhanced has proven recognize three types radiographic 99% accuracy, typical pneumonia. validity performance metrics proposed were validated publicly available data sets. suggested outperforms competing in diagnosing primary outcomes this result an average F-score (95%, 97%). In event healthy viral infections, more efficient than existing methodologies coronavirus detection. created appropriate recognition pre-training. traditional strategies categorization illnesses.

Language: Английский

Citations

36
A novel soft attention-based multi-modal deep learning framework for multi-label skin lesion classification DOI
Aslı Nur Ömeroğlu, Hussein M.A. Mohammed,

Emin Argun Oral

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 120, P. 105897 - 105897

Published: Jan. 28, 2023

Language: Английский

Citations

30
Incremental learning-based cascaded model for detection and localization of tuberculosis from chest x-ray images DOI
Satvik Vats, Vikrant Sharma, Karan Singh

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 122129 - 122129

Published: Oct. 14, 2023

Language: Английский

Citations

30
Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans DOI Creative Commons
Hassaan Malik, Tayyaba Anees, Ahmad Naeem

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(2), P. 203 - 203

Published: Feb. 3, 2023

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed isolate COVID-19. When it comes determining identity COVID-19, one most significant obstacles that researchers overcome is propagation virus, in addition dearth trustworthy testing models. This problem continues difficult for clinicians deal with. The use AI image processing has made formerly insurmountable challenge finding COVID-19 situations more manageable. In real world, there handled about difficulties sharing data between hospitals while still honoring privacy concerns organizations. training global deep learning (DL) model, crucial handle fundamental such as user collaborative model development. For this study, novel framework designed compiles information from five different databases (several hospitals) edifies using blockchain-based federated (FL). validated through blockchain technology (BCT), FL trains on scale maintaining secrecy proposed divided into three parts. First, we provide method normalization can diversity collected sources several computed tomography (CT) scanners. Second, categorize patients, ensemble capsule network (CapsNet) with incremental extreme machines (IELMs). Thirdly, interactively BCT anonymity. Extensive tests employing chest CT scans comparison classification performance DL algorithms predicting protecting variety users, were undertaken. Our findings indicate improved effectiveness identifying patients achieved an accuracy 98.99%. Thus, our provides substantial aid medical practitioners their diagnosis

Language: Английский

Citations

24