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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kesavan Suresh Manic
National University of Science and Technology
Computer Science & IT Engineering

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COVID-19 detection from medical images using hummingbird-optimized VGG16 framework Roshima Biju; Warish Patel; Kesavan Suresh Manic
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4806-4818

Abstract

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created a major diagnostic challenge worldwide, highlighting the need for reliable automated approaches to assist clinicians in detecting coronavirus disease 2019 (COVID-19) from medical images. This study introduces a unified detection framework designed to deliver robust and accurate classification performance across different imaging modalities. The proposed model was developed and evaluated on computed tomography (CT) and chest X-ray datasets. The workflow comprises five key stages: image acquisition and resizing, deep-feature extraction, handcrafted feature extraction, feature optimization, and binary classification. The proposed system achieved high diagnostic performance, yielding approximately 99.33% accuracy for CT images and about 99.40% accuracy for X-ray images. The results demonstrate the framework’s robustness and its potential as effective tool for computer-aided COVID-19 diagnosis.
Co-Authors Roshima Biju Warish Patel