Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges DOI Creative Commons
Muhammad Waqar Azeem, Shumaila Javaid, Ruhul Amin Khalil

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(7), P. 850 - 850

Published: July 18, 2023

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment care has increased in popularity enhanced patient safety quality care. Therefore, this paper reviews the critical role ANNs providing valuable insights patients’ healthcare efficient disease diagnosis. We study different types existing literature that advance ANNs’ adaptation complex applications. Specifically, we investigate advances predicting viral, cancer, skin, COVID-19 diseases. Furthermore, propose deep convolutional network (CNN) model called ConXNet, based on chest radiography images, improve detection accuracy disease. ConXNet is trained tested using image dataset obtained from Kaggle, achieving more than 97% 98% precision, which better other state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, COVID-Net, having 93.1%, 94.10%, 84.76%, 90% 94%, 95%, 85%, 92% respectively. The results show performed significantly well relatively compared with aforementioned models. Moreover, reduces time complexity by dropout layers batch normalization techniques. Finally, highlight future research directions challenges, algorithms, insufficient available data, privacy security, integration biosensing ANNs. These require considerable attention improving scope diagnostic

Language: Английский

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images DOI Open Access
Hatice Çatal Reis, Veysel Turk

Artificial Intelligence in Medicine, Journal Year: 2022, Volume and Issue: 134, P. 102427 - 102427

Published: Oct. 17, 2022

Language: Английский

Citations

31
The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models DOI
Hari Mohan, Kalyan Chatterjee, Serhii Dashkevych

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 150, P. 106142 - 106142

Published: Sept. 22, 2022

Language: Английский

Citations

29
Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification DOI
Caixia Dong, Duwei Dai, Yizhi Zhang

et al.

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 152, P. 106321 - 106321

Published: Nov. 17, 2022

Language: Английский

Citations

28
Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss DOI
Yufei Jin, Huijuan Lu, Wenjie Zhu

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 157, P. 106683 - 106683

Published: Feb. 15, 2023

Language: Английский

Citations

22
Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges DOI Creative Commons
Muhammad Waqar Azeem, Shumaila Javaid, Ruhul Amin Khalil

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(7), P. 850 - 850

Published: July 18, 2023

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment care has increased in popularity enhanced patient safety quality care. Therefore, this paper reviews the critical role ANNs providing valuable insights patients’ healthcare efficient disease diagnosis. We study different types existing literature that advance ANNs’ adaptation complex applications. Specifically, we investigate advances predicting viral, cancer, skin, COVID-19 diseases. Furthermore, propose deep convolutional network (CNN) model called ConXNet, based on chest radiography images, improve detection accuracy disease. ConXNet is trained tested using image dataset obtained from Kaggle, achieving more than 97% 98% precision, which better other state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, COVID-Net, having 93.1%, 94.10%, 84.76%, 90% 94%, 95%, 85%, 92% respectively. The results show performed significantly well relatively compared with aforementioned models. Moreover, reduces time complexity by dropout layers batch normalization techniques. Finally, highlight future research directions challenges, algorithms, insufficient available data, privacy security, integration biosensing ANNs. These require considerable attention improving scope diagnostic

Language: Английский

Citations

21