Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Evaluating Deep Learning-Based Image Classification Techniques for Pneumonia Detection in CT Scans

Authors
P. Arthi Devarani1, *, M. Sharu Shree2, R. Arun Prathap3
1Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, RSM Nagar, Thiruvallur, India
2Department of Electronics and Communication Engineering, R.M.K. College of Engineering and Technology, RSM Nagar, Thiruvallur, India
3Department of Civil Engineering, R.M.K. Engineering College, Thiruvallur, India
*Corresponding author. Email: arthidevaraniece@rmkcet.ac.in
Corresponding Author
P. Arthi Devarani
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_64How to use a DOI?
Keywords
Deep Learning; Image Classification Models; Performance Analysis
Abstract

Deep learning-based image classification models have been established as a potent approach in the medical imaging domain as they are able to provide higher-level accuracy for disease diagnostic tasks. Convolutional Neural Network (CNN) architectures have shown great promise from among these in parsing radiological images like CT scans. The study highlights the overall performance analysis of several state-of-the-art deep learning models for pneumonia detection from CT scan images. The models evaluated include EfficientNetB0, EfficientNetV2B3, ResNet152V2, ResNet50V2, MobileNetV2, VGG16, ConvNeXtBase, DenseNet201, InceptionResNetV2, Xception. Among all, Dense- Net201 demonstrated superior performance, achieving a training accuracy of 99.75%, validation accuracy of 97.89%, precision, recall and F1-score of 95%, indicating its effectiveness and robustness in pneumonia detection. The outcomes of this research can help researchers and health practitioners in choosing the best models for different healthcare analysis models.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_64How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. Arthi Devarani
AU  - M. Sharu Shree
AU  - R. Arun Prathap
PY  - 2025
DA  - 2025/10/31
TI  - Evaluating Deep Learning-Based Image Classification Techniques for Pneumonia Detection in CT Scans
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 787
EP  - 799
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_64
DO  - 10.2991/978-94-6463-866-0_64
ID  - Devarani2025
ER  -