CancerGuard: A Deep Learning Approach to Lung Cancer Detection

DOI

10.2991/978-94-6239-664-7_6How to use a DOI?

Keywords

Deep Learning; Lung cancer; MobileNetV2; InceptionV3; VGG16; Histopathology image

Abstract

Lung cancer remains the leading cause of cancer-related mortality around the world, requiring headways in early discovery methods to progress understanding results. This study explores the efficacy of deep learning models, particularly InceptionV3, VGG16, and MobileNetV2, within the detection and classification of lung cancer through histopathology images. Utilizing a dataset comprising various lung cancer subtypes, these models were trained and validated, demonstrating amazing diagnostic accuracy. The results highlight the potential of deep learning to enhance lung cancer diagnostics significantly, outperforming conventional diagnostic strategies in both speed and precision. This paper discusses the models’ performance metrics, including precision, recall, F1 score, and overall accuracy, which substantiate the robustness and reliability of deep learning in medical imaging. The dataset was open source which contains 15000 images and the accuracy of the proposed model is 99%. The findings advocate for the integration of deep learning technologies in clinical settings to encourage early and exact lung cancer location, subsequently possibly expanding survival rates.

Copyright

© 2026 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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TY  - CONF
AU  - Md. Shazedur Rahman
AU  - Md Shahriar Mannan Prottoy
AU  - Mahtab Chowdhury
AU  - Tasbih Tahlil Nidhi
AU  - Azim Ullah Tamim
AU  - Sadman Sadik Khan
PY  - 2026
DA  - 2026/06/08
TI  - CancerGuard: A Deep Learning Approach to Lung Cancer Detection
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 62
EP  - 75
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_6
DO  - 10.2991/978-94-6239-664-7_6
ID  - Rahman2026
ER  -