Enhancing COVID-19 Diagnosis with Deep Learning Models DenseNet and ResNet on Medical Imaging Data

S. Chowdri, S. Shanthi, R. Dhanaeswaran, P. Giridar Prasad, K. Nirmala Devi, A. Kavidha

2025

Abstract

The COVID-19 pandemic has put immense pressure on health systems worldwide. Early and accurate diagnosis is thus an important modality of management of the disease. RT-PCR tests are the gold standard for diagnosis but suffer from a number of limitations such as high processing time and occasional false negatives. Diagnostic imaging via chest X rays and CT scans offers a rapid, non-invasive alternative for detecting COVID-19-induced lung abnormalities. This study evaluates the performance of various configurations of DenseNet (121, 169, 201) and ResNet-152 for automated COVID-19 detection using chest X-rays and CT scans. More in particular, DenseNet-201 yielded a good result of approximately 96% accuracy for CT scans and 99% for X-rays when trained with the Adam optimizer using a batch size of 32. It highlights that the choice of optimizer and batch size has paramount importance. DenseNet-201’s efficient gradient flow, feature reuse, and parameter utilization make it especially suitable for medical imaging applications with limited annotated datasets. Its robust feature extraction capabilities position it as a reliable diagnostic tool, potentially enhancing clinical workflows and accelerating COVID-19 diagnosis. This study underscores DenseNet-201’s potential to improve patient outcomes and pandemic management through accurate, automated medical image analysis.

Paper Citation

in Harvard Style

Chowdri S., Shanthi S., Dhanaeswaran R., Giridar Prasad P., Devi K. and Kavidha A. (2025). Enhancing COVID-19 Diagnosis with Deep Learning Models DenseNet and ResNet on Medical Imaging Data. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 506-513. DOI: 10.5220/0013595700004664

in Bibtex Style

@conference{incoft25,
author={S. Chowdri and S. Shanthi and R. Dhanaeswaran and P. Giridar Prasad and K. Nirmala Devi and A. Kavidha},
title={Enhancing COVID-19 Diagnosis with Deep Learning Models DenseNet and ResNet on Medical Imaging Data},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={506-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013595700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Enhancing COVID-19 Diagnosis with Deep Learning Models DenseNet and ResNet on Medical Imaging Data
SN - 978-989-758-763-4
AU - Chowdri S.
AU - Shanthi S.
AU - Dhanaeswaran R.
AU - Giridar Prasad P.
AU - Devi K.
AU - Kavidha A.
PY - 2025
SP - 506
EP - 513
DO - 10.5220/0013595700004664
PB - SciTePress