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Evaluating VGG16, ResNet50, and Xception for COVID-19 Detection via a Web-Based Deep Learning Interface
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Author(s): Snehal Rahul Rathi (Vishwakarma Institute of Technology, Pune, India), Chitra Joshi (Vishwakarma Institute of Information Technology, Pune, India), Atharva Pardeshi (Vishwakarma Institute of Information Technology, Pune, India)and Anushka Bhagwat (Vishwakarma Institute of Information Technology, Pune, India)
Copyright: 2026
Pages: 26
Source title:
Revolutionizing Medicine With Autonomous Robotics
Source Author(s)/Editor(s): Dattatray Gopal Takale (Vishwakarma Institute of Information Technology, India), Parikshit N. Mahalle (Vishwakarma Institute of Information Technology, India), Bipin Sule (Vishwakarma Institute of Technology, India), Vivek S. Deshpande (Vishwakarma Institute of Information Technology, India)and Nilesh P. Sable (Vishwakarma Institute of Information Technology, India)
DOI: 10.4018/979-8-3373-0179-2.ch007
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Abstract
The global outbreak of COVID-19 has emphasized the need for fast and accurate diagnostic methods, especially in areas where advanced testing facilities are scarce or unavailable. This research proposes a deep learning-based method for the binary classification of chest X-ray images aimed at detecting COVID-19 infections. We evaluate and compare the performance of three prominent Convolutional Neural Network (CNN) architectures ResNet50, VGG16, and Xception trained on publicly available chest radiograph datasets. Additionally, a confusion matrix, training versus validation accuracy plots, and the ROC curve are employed to gain deeper insights into the model's effectiveness and diagnostic capability. To enhance usability, a user-friendly web interface has also been developed, enabling users to upload images and obtain real-time classification results. Experimental findings indicate that all three models perform well in distinguishing COVID-19 cases, with the Xception model achieving the highest accuracy.
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