Title: Detection and classification of COVID-19 using supervised deep learning on MRI images

Authors: J. Chinna Babu; Mudassir Khan; Mallikharjuna Rao Nuka; C.H. Nagaraju

Addresses: Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India ' Department of Computer Science, College of Science and Arts Tanumah, King Khalid University, Abha, Saudi Arabia ' Department of Computer Applications, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India ' Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India

Abstract: Healthcare services in many parts of the world, but especially in emerging countries, have been made aware of the risks presented by the COVID-19 pandemic. In areas where bulk traditional testing is not practical, new computer-assisted diagnosis methods are clearly needed to provide speedy and cost-effective screening. Pulmonary ultrasonography can be used to diagnose lung disease since it is portable, easy to clean, inexpensive, and non-invasive. In recent years, computer-assisted analysis of lung ultrasound images has showed considerable promise for identifying respiratory disorders, including COVID-19 screening and diagnosis. Detecting COVID-19 infections from lung ultrasound images using deep-learning algorithms and comparing their results. It was possible to use a variety of pre-trained deep learning architectures to this problem. There are 3,326 lung ultrasound images in the POCUS dataset, which we used to train and fine-tune our algorithm. Computed tomography (CT) proved useful in the diagnosis of corona virus infection particularly in the pandemic of new corona virus (COVID-19). Radiation from patients who underwent CT scans experienced alterations that were comparable to those seen in MRI scans. A chest MRI should be performed if a CT scan is unavailable, according to the study's findings.

Keywords: COVID-19; deep learning; DL; supervised learning; machine learning.

DOI: 10.1504/IJBRA.2023.135362

International Journal of Bioinformatics Research and Applications, 2023 Vol.19 No.4, pp.233 - 251

Received: 04 Feb 2023
Accepted: 28 Apr 2023
Published online: 06 Dec 2023 *

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