Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model for detecting COVID-19 from CT scan images, designed to assist overburdened medical professionals. Our proposed model leverages the strengths of VGG16, DenseNet121, and MobileNetV2 to extract features, followed by Principal Component Analysis (PCA) for dimensionality reduction, after which the features are stacked and classified using a Support Vector Classifier (SVC). We conducted comparative analysis between the proposed hybrid model and individual pre-trained CNN models, using a dataset of 2,108 training images and 373 test images comprising both COVID-positive and non-COVID images. Our proposed hybrid model achieved an accuracy of 98.93%, outperforming the individual models in terms of precision, recall, F1 scores, and ROC curve performance.
@article{arxiv.2501.17160,
title = {A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images},
author = {Suresh Babu Nettur and Shanthi Karpurapu and Unnati Nettur and Likhit Sagar Gajja and Sravanthy Myneni and Akhil Dusi and Lalithya Posham},
journal= {arXiv preprint arXiv:2501.17160},
year = {2025}
}
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Corresponding authors: Shanthi Karpurapu ([email protected]), Suresh Babu Nettur ([email protected]) Shanthi Karpurapu and Suresh Babu Nettur are co-first authors