Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study
BackgroundCOVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art tec...
Main Authors: | Albahli, Saleh, Yar, Ghulam Nabi Ahmad Hassan |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2021-02-01
|
Series: | Journal of Medical Internet Research |
Online Access: | http://www.jmir.org/2021/2/e23693/ |
Similar Items
-
AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays
by: Saleh Albahli, et al.
Published: (2021-04-01) -
AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease
by: Saleh Albahli, et al.
Published: (2022-08-01) -
Fast and accurate interpretation of workload classification model
by: Sooyeon Shim, et al.
Published: (2023-01-01) -
Fast and accurate interpretation of workload classification model.
by: Sooyeon Shim, et al.
Published: (2023-01-01) -
Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification
by: Saleh Albahli, et al.
Published: (2022-10-01)