Title: Modified VGG-16 model for COVID-19 chest X-ray images: optimal binary severity assessment

Authors: Manoranjan Dash

Addresses: Artificial Intelligence Department, Anurag University, Hyderabad, India

Abstract: A pandemic caused by a virus known as COVID-19 has swept across the globe. One potential weapon in the fight against COVID-19 could be early detection through the use of chest X-ray images. In this paper, I have used modified VGG-16 deep learning model for binary classification of COVID-19 chest X-ray images. There are 16 weight layers in the standard VGG-16 model. In the suggested modified VGG model, the total number of weight layers has been reduced from 16 to 9 (eight convolutional layers and one fully connected layer). According to the results, the modified VGG-16 model performs better than the other three models (CNN, KNN and VGG-16) in terms of quantitative measures of accuracy, sensitivity and specificity. The dataset used for the proposed work consists of 24,000 chest X-ray images of lung collected from online depository comprising of 12,000 for each class (healthy and pneumonia).

Keywords: deep learning; classification; COVID-19; SARS-CoV-2; modified VGG-16.

DOI: 10.1504/IJDMB.2026.150962

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.1/2, pp.18 - 27

Received: 30 Oct 2023
Accepted: 15 May 2024
Published online: 06 Jan 2026 *

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