Analyzing CNN Applications in Lung Cancer Detection and Diagnosis: Advancements, Challenges and Future Prospects

DOI

10.2991/978-94-6463-716-8_17How to use a DOI?

Keywords

Deep Learning (DL) Techniques; Deep Convolutional Neural Networks (CNNs); Histopathology; Lung Cancer detection; Medical Image Analysis

Abstract

CNN demonstrated significant potential in lung cancer recognition and identification through the analysis of medical imaging data. This abstract shows brief of the progress, challenges, and future application of CNN in this domain. CNNs have shown remarkable capabilities in accurately identifying lung nodules and distinguishing between benign and malignant lesions using data from different imaging techniques for example CT scans and X-rays. Lung cancer patient prognosis, prediction and early detection have improved as a result of their capacity to recognize intricate patterns and features in images. Despite their successes, CNNs face several challenges in lung cancer diagnosis. Unpredictability in image quality, size, and resolution, as well as presence of artifacts and overlapping structures, pose significant obstacles to accurate detection and classification. Addressing these makes collective efforts from clinicians, researchers, and technologists. Additionally, advancements in explainable AI method will develop the interpretability of CNN-based diagnosis, fostering trust and acceptance among healthcare professionals. In conclusion, CNNs offer invaluable opportunities for improving lung cancer detection and diagnosis. Overcoming existing challenges and capitalizing on future opportunities will drive the development of more efficient and accurate CNN-based approaches, ultimately benefiting lung cancer patients and healthcare providers.

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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TY  - CONF
AU  - Priyanka Khabiya
AU  - Firoj Parwej
PY  - 2025
DA  - 2025/05/26
TI  - Analyzing CNN Applications in Lung Cancer Detection and Diagnosis: Advancements, Challenges and Future Prospects
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 200
EP  - 211
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_17
DO  - 10.2991/978-94-6463-716-8_17
ID  - Khabiya2025
ER  -