COVID‐19 diagnosis system by deep learning approaches. Issue 3 (29th July 2021)

• Record Type: Journal Article
• Authors: Bhuyan, Hemanta Kumar; Chakraborty, Chinmay; Shelke, Yogesh; Pani, Subhendu Kumar
• Other Names: Gupta Deepak guestEditor.; Kose Utku guestEditor.; Castillo Oscar guestEditor.; Al‐Turjman Fadi guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-29
• Subjects: COVID‐19 -- quantitative evaluation -- respiratory diagnosis -- X‐Rays or CT images; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12776
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The novel coronavirus disease 2019 (COVID‐19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID‐19 patients, which are not effective. The above complex circumstances need to detect suspected COVID‐19 patients based on routine techniques like chest X‐Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID‐19 or Non‐COVID‐19 patients with a full‐resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID‐19 patient dataset. The evaluation results are generated using a fourfold cross‐validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1‐score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.