Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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Explainable Lung Cancer Classification using VGG16, and Grad-CAM

Authors
Souaad Hamza-Cherif1, Taleb Tariq1, *, Zineb Aziza Elaouaber1, Messadi Mohammed1
1Biomedical Engineering Laboratory, Abou Bekr Belkaid University of Tlemcen, Tlemcen, Algeria
*Corresponding author. Email: tariq.taleb@univ-tlemcen.dz
Corresponding Author
Taleb Tariq
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_2How to use a DOI?
Keywords
Lung cancer; CT images; VGG16; XAI; classification; Grad-CAM
Abstract

Lung cancer ranks among the foremost causes of cancer-related mortality, and early identification is essential for enhancing survival rates. This research introduces a deep learning methodology employing a customised VGG16 convolutional neural network model for the autonomous classification of lung cancer. The model categorises CT scan pictures into three classes: normal, benign, and malignant. To improve the model transparency, we use Grad-CAM, an explainability technique that visualises the significant regions of an image impacting the model decision. Our experiments, performed on the IQ-OTH/NCCD dataset, indicate that the suggested method attains a classification accuracy of 96%, hence confirming its efficacy in lung cancer diagnosis. Employing Grad-CAM yields significant insights into the decision-making process, fostering trust and interpretability in AI-driven healthcare systems.

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 First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_2How 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  - Souaad Hamza-Cherif
AU  - Taleb Tariq
AU  - Zineb Aziza Elaouaber
AU  - Messadi Mohammed
PY  - 2025
DA  - 2025/08/05
TI  - Explainable Lung Cancer Classification using VGG16, and Grad-CAM
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 5
EP  - 11
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_2
DO  - 10.2991/978-94-6463-805-9_2
ID  - Hamza-Cherif2025
ER  -