Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Measurement of Necrotic Lung Lesions Distance in CT Images Using Optimized Contrastive Learning

Authors
M. Shanmuga Sundari 1, *, Vyshnavi Kunta1, Sri Venkata Sai Pavani Akula1, Aniya Afnan1
1Computer Science Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India
*Corresponding author. Email: sundari.m@bvrithyderabad.edu.in
Corresponding Author
M. Shanmuga Sundari
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_281How to use a DOI?
Keywords
Lung Lesion Segmentation; Optimized contrastive learning; Medical Image Analysis; Necrotic Lung Lesions; Lesion Distance Measurement
Abstract

Precise identification and quantification of necrotic lung lesions in CT scans are important for analyzing lesion traits and monitoring disease advancement; how- ever, conventional techniques frequently face challenges in extracting detailed features and depend significantly on manual input. An optimized contrastive learning approach is proposed to enhance feature extraction and enable pre cise segmentation of necrotic lung lesions. By fine-tuning pre-trained networks with con trastiveloss functions and incorporating advanced data augmentation techniques, the system improves robustness and generalization across diverse datasets. It automates the segmentation and measurement of inter-lesion distances, reducing manual effort and providing quantitative metrics, such as the Dice similarity coefficient and the average distance error, for better analysis of lesion characteristics.

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 International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_281How 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  - M. Shanmuga  Sundari 
AU  - Vyshnavi Kunta
AU  - Sri Venkata Sai Pavani Akula
AU  - Aniya Afnan
PY  - 2025
DA  - 2025/11/04
TI  - Measurement of Necrotic Lung Lesions Distance in CT Images Using Optimized Contrastive Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3364
EP  - 3372
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_281
DO  - 10.2991/978-94-6463-858-5_281
ID  - Sundari2025
ER  -