OVASO: Integrated binary CNN models to classify COVID‐19, pneumonia and healthy lung in X‐ray images. Issue 4 (9th May 2022)

• Record Type: Journal Article
• Authors: Jeong, Hyunsu; Kim, Hyunwook; Yoon, Jiwon; Go, Kyungsup; Gwak, Jeonghwan
• Is Part Of: International journal of imaging systems and technology. Volume 32:Issue 4(2022)
• Page Start: 1035
• Page End: 1048
• Publication Date: 2022-05-09
• Subjects: class imbalance -- classification -- convolutional neural networks -- COVID‐19 -- deep learning -- medical imaging -- multi‐class -- one‐versus‐all classification -- one‐versus‐one classification -- transfer learning; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22742
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Several radiologists have paid attention to computer‐aided detection (CAD) systems which assist in classifying diseases on chest x‐ray (CXR). Recently, with the outbreak of COVID‐19, CAD based on deep learning has an important role in screening COVID‐19 on CXR. However, imbalanced training datasets such as COVID‐19 datasets, COVID‐19 (473), pneumonia (5458), and normal (7966) cause difficulty in classification. In this paper, we suggest a new evaluation approach, OVASO, that selectively combines one‐versus‐all (OVA) classifier and one‐versus‐one (OVO) to overcome class imbalance caused by the lower number of COVID‐19 training datasets. In addition, as part of efforts to properly apply transfer learning, we initialized batch normalization (BN) values including γ and β from the viewpoint of transfer learning and found that appropriate initialization at all binary models, OVASO's components, usually increased the binary models' performance. As a result, the proposed OVASO model achieved improved accuracy and F1‐score of 95.33% and 95.88%, respectively. Furthermore, the suggested OVASO performed similarly to COVID‐Net, which is the current state‐of‐the‐art model for classifying COVID‐19 on CXR.