Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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Optimized Lung Cancer Detection and Classification Using Attention Based CNN Driven Improved Chan-Vese Algorithm

Authors
A. J. Rajeswari Joe1, *, V. Thamilarasi2, Akhilesh Singh3, S. Udhayakumar4, R. Rajiniganth5, Ezhil Arasu6
1Department of Computer Science (PG & Research), Thiruthangal Nadar College, Chennai, India
2Assistant Professor of Computer Science, Sri Sarada College for Women (Autonomous), Salem, Tamilnadu, India
3Department of Electrical Engineering, Uttarakhand Technical University Campus Institute (NPSEI), Pithoragarh, Uttarakhand, India
4Assistant Professor, Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, India
5Assistant Professor, Department of Computer Science and Technology, SNS College of Technology, Coimbatore, India
6Assistant Professor, SNS College of Physiotherapy, SNS Kalvi Nagar, Sathy Main Road, Kurumbapalayam, Coimbatore, India
Corresponding Author
A. J. Rajeswari Joe
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_63How to use a DOI?
Keywords
Boosted Adaptive Diffusion filter; fuzzy Thresholding; optimized chan-vese algorithm; 3D-CNN
Abstract

Globally, lung illness is the leading cause of death. Lung cancer is the leading cause of mortality for both people and has the most startling death rate of all tumour kinds. An estimated 1.1 million individuals die from this disease each year, while an estimated 1.2 million people receive a routine diagnosis. Early cancer detection increases the survival rate. It is difficult to immediately identify malignant growths in the lungs. Lung cancer can be detected using a variety of imaging techniques. A radiologist can predict and detect abnormalities with speed and accuracy by using a computer-aided diagnosis technique. The main goal of the CAD systems is to locate lung nodules. Since the course of treatment depends on the stage of the disease, it is imperative to focus on staging lung cancer as soon as it is discovered. The accuracy of lung cancer staging and nodule segmentation is one of the primary issues with existing CAD systems. The primary objective of the proposed work is to segment the lung nodule from the CT image and classify it as malignant or non-cancerous in order to diagnose the site of cancer with more sensitivity, specificity, and accuracy than current methods.

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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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_63How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - A. J. Rajeswari Joe
AU  - V. Thamilarasi
AU  - Akhilesh Singh
AU  - S. Udhayakumar
AU  - R. Rajiniganth
AU  - Ezhil Arasu
PY  - 2026
DA  - 2026/04/24
TI  - Optimized Lung Cancer Detection and Classification Using Attention Based CNN Driven Improved Chan-Vese Algorithm
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 803
EP  - 817
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_63
DO  - 10.2991/978-94-6239-654-8_63
ID  - Joe2026
ER  -