Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine

S Roy, T Meena, SJ Lim - Diagnostics, 2022 - mdpi.com
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …

Review on the evaluation and development of artificial intelligence for COVID-19 containment

MM Hasan, MU Islam, MJ Sadeq, WK Fung, J Uddin - Sensors, 2023 - mdpi.com
Artificial intelligence has significantly enhanced the research paradigm and spectrum with a
substantiated promise of continuous applicability in the real world domain. Artificial …

Real-time polyp detection, localization and segmentation in colonoscopy using deep learning

D Jha, S Ali, NK Tomar, HD Johansen… - Ieee …, 2021 - ieeexplore.ieee.org
Computer-aided detection, localization, and segmentation methods can help improve
colonoscopy procedures. Even though many methods have been built to tackle automatic …

Explainable artificial intelligence methods in combating pandemics: A systematic review

F Giuste, W Shi, Y Zhu, T Naren, M Isgut… - IEEE Reviews in …, 2022 - ieeexplore.ieee.org
Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-
based solutions to COVID-19 challenges during the pandemic, few have made a significant …

Deep learning models for COVID-19 infected area segmentation in CT images

A Voulodimos, E Protopapadakis… - Proceedings of the 14th …, 2021 - dl.acm.org
Recent studies indicated that detecting radiographic patterns on CT chest scans can yield
high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the …

COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework

J Liu, B Dong, S Wang, H Cui, DP Fan, J Ma… - Medical image …, 2021 - Elsevier
With the global outbreak of COVID-19 in early 2020, rapid diagnosis of COVID-19 has
become the urgent need to control the spread of the epidemic. In clinical settings, lung …

Machine learning-based research for COVID-19 detection, diagnosis, and prediction: A survey

Y Meraihi, AB Gabis, S Mirjalili, A Ramdane-Cherif… - SN computer …, 2022 - Springer
The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted
the whole world. The absence of treatment has motivated research in all fields to deal with it …

An effective deep neural network for lung lesions segmentation from COVID-19 CT images

C Chen, K Zhou, M Zha, X Qu, X Guo… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images
can help to establish a quantitative model for diagnosis and treatment. For this reason, this …

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

H Hassan, Z Ren, C Zhou, MA Khan, Y Pan… - Computer Methods and …, 2022 - Elsevier
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract
features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests …

Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks

H Hassan, Z Ren, H Zhao, S Huang, D Li… - Computers in biology …, 2022 - Elsevier
This article presents a systematic overview of artificial intelligence (AI) and computer vision
strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized …