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Computer Sciences & Mathematics Forum
Volume 12
Issue 1
10.3390/cmsf2025012007
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Proceeding Paper

Deep Learning Approaches to Chronic Venous Disease Classification †

by
Ankur Goyal
1,*,
Vikas Honmane
2,
Kumarsagar Dange
2 and
Shiv Kant
3
1
Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune Symbiosis International Deemed University, Pune 412115, India
2
Annasaheb Dange College of Engineering & Technology, Sangli 416301, India
3
Department of Computer Science and Engineering (Artificial Intelligence & Data Science), Greater Noida Institute of Technology (GNIOT), Greater Noida 201310, India
*
Author to whom correspondence should be addressed.
†
Presented at the First International Conference on Computational Intelligence and Soft Computing (CISCom 2025), Melaka, Malaysia, 26–27 November 2025.
Comput. Sci. Math. Forum 2025, 12(1), 7; https://doi.org/10.3390/cmsf2025012007
Published: 18 December 2025
(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))
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Abstract

Millions of people suffer from chronic venous disease (CVD), a common vascular condition that frequently causes pain, edema, and skin ulcers. For treatment to be effective, its stages must be accurately and promptly classified. This study offers a deep learning-based framework for classifying CVD stages using medical images, such as limb photos or ultrasound scans. For training and assessment, convolutional neural networks (CNNs) are used in conjunction with pre-trained models like ResNet, VGG, and Efficient Net. Metrics like accuracy, precision, recall, and F1-score are used to evaluate the model’s performance. The encouraging findings suggest that deep learning tools can greatly facilitate the diagnosis of CVD and may be integrated into clinical decision support systems for quicker, more precise evaluations.
Keywords: CVD; CNNs; VGG; deep learning; ResNet; accuracy; performance metrices CVD; CNNs; VGG; deep learning; ResNet; accuracy; performance metrices

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MDPI and ACS Style

Goyal, A.; Honmane, V.; Dange, K.; Kant, S. Deep Learning Approaches to Chronic Venous Disease Classification. Comput. Sci. Math. Forum 2025, 12, 7. https://doi.org/10.3390/cmsf2025012007

AMA Style

Goyal A, Honmane V, Dange K, Kant S. Deep Learning Approaches to Chronic Venous Disease Classification. Computer Sciences & Mathematics Forum. 2025; 12(1):7. https://doi.org/10.3390/cmsf2025012007

Chicago/Turabian Style

Goyal, Ankur, Vikas Honmane, Kumarsagar Dange, and Shiv Kant. 2025. "Deep Learning Approaches to Chronic Venous Disease Classification" Computer Sciences & Mathematics Forum 12, no. 1: 7. https://doi.org/10.3390/cmsf2025012007

APA Style

Goyal, A., Honmane, V., Dange, K., & Kant, S. (2025). Deep Learning Approaches to Chronic Venous Disease Classification. Computer Sciences & Mathematics Forum, 12(1), 7. https://doi.org/10.3390/cmsf2025012007

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