Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks

Ghulam Gilanie; Usama Ijaz Bajwa; Mustansar Mahmood Waraich; Mutyyba Asghar; Rehana Kousar; Adnan Kashif; Rabab Shereen Aslam; Muhammad Mohsin Qasim; Hamza Rafique

doi:10.1016/j.bspc.2021.102490

Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks

Biomed Signal Process Control. 2021 Apr:66:102490. doi: 10.1016/j.bspc.2021.102490. Epub 2021 Feb 10.

Authors

Ghulam Gilanie¹, Usama Ijaz Bajwa¹, Mustansar Mahmood Waraich², Mutyyba Asghar³, Rehana Kousar³, Adnan Kashif³, Rabab Shereen Aslam³, Muhammad Mohsin Qasim³, Hamza Rafique⁴

Affiliations

¹ Department of Computer Science, Faculty of Computing, The Islamia University of Bahawalpur, Pakistan.
² Department of Radiology and Diagnostic Images, Bahawal Victoria Hospital, Bahawalpur, Pakistan.
³ Department of Computer Science, National College of Business Administration & Economics, Bahawalpur Sub Campus, Bahawalpur, Pakistan.
⁴ Innovative Research and Development Group (IRDG), Bahawalpur, Pakistan.

Abstract

Coronavirus disease (Covid-19) has been spreading all over the world and its diagnosis is attracting more research every moment. It is need of the hour to develop automated methods, which could detect this disease at its early stage, in a non-invasive way and within lesser time. Currently, medical specialists are analyzing Computed Tomography (CT), X-Ray, and Ultrasound (US) images or conducting Polymerase Chain Reaction (PCR) for its confirmation on manual basis. In Pakistan, CT scanners are available in most hospitals at district level, while X-Ray machines are available in all tehsil (large urban towns) level hospitals. Being widely used imaging modalities to analyze chest related diseases, produce large volume of medical data each moment clinical environments. Since automatic, time efficient and reliable methods for Covid-19 detection are required as alternate methods, therefore an automatic method of Covid-19 detection using Convolutional Neural Networks (CNN) has been proposed. Three publically available and a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur (BVHB), Pakistan have been used. The proposed method achieved on average accuracy (96.68 %), specificity (95.65 %), and sensitivity (96.24 %). Proposed model is trained on a large dataset and is being used at the Radiology Department, (BVHB), Pakistan.

Keywords: Chest radiology images; Covid-19 detection; Machine learning.