An Effective Approach to SARS-CoV-2 Diagnosis by Developing the CNN Algorithm

Saja Salim  Mohammed; Ziadoon W.  Salman; Abbas Alaa  Mahdi

doi:10.24996/ijs.2024.65.9.38

Authors

Saja Salim Mohammed Department of Theoretical Sciences, College of Physical Education and Sport Sciences, University of Diyala, Diyala , Iraq https://orcid.org/0009-0001-0989-1845
Ziadoon W. Salman Directorate of Baghdad Education, Rusafa-2, Baghdad, Iraq
Abbas Alaa Mahdi Muqdadiya Education Department, Directorate General of Education Diyala, Diyala, Iraq

DOI:

https://doi.org/10.24996/ijs.2024.65.9.38

Keywords:

Magnetic Resonance Imaging (MRI), Deep Learning (DL) Effective Approach, Developing CNN Algorithm, VGG16 Model, SARS-COV-2 Disease

Abstract

Today, SARS is a viral infection of the respiratory system that causes damage to the alveoli. The virus that causes severe acute respiratory syndrome belongs to the family of coronaviruses, commonly known as COVID, which is the same family of viruses that cause common colds. The increasing use of artificial intelligence (AI) and machine learning (ML) nowadays provides excellent opportunities to support and improve medical services in societies. In this paper, a developing model of deep learning (DL) is designed and implemented to classify SARS-CoV-2 disease images into two classes: COVID and non-COVID, with an accuracy of 99%. From a performance and accuracy perspective, the artificial convolutional neural network (CNN)-based detection model that was developed achieved better performance than the pre-trained VGG16 model and other learning models with a similar purpose. As a result, this designed model is able to aid medical professionals by providing a tool that facilitates the detection of this sickness and, consequently, the provision of appropriate medical care.