Open Access

ARTICLE

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Aghila Rajagopal¹, Sultan Ahmad^2,*, Sudan Jha³, Ramachandran Alagarsamy⁴, Abdullah Alharbi⁵, Bader Alouffi⁶

1 Department of Computer Science and Business Systems, Sethu Institute of Technology, Kariapatti, Virudhunagar, 626115, Tamilnadu, India
2 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia
3 Department of Computer Science and Engineering, School of Engineering, Kathmandu University, Banepa, Kathmandu, Nepal
4 Department of CSE, University College of Engineering, Panruti, Tamilnadu, India
5 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
6 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Sultan Ahmad. Email:

Computer Systems Science and Engineering 2023, 45(3), 3215-3229. https://doi.org/10.32604/csse.2023.025705

Received 02 December 2021; Accepted 04 July 2022; Issue published 21 December 2022

Abstract

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity.

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