Enhancing The Predicition Accuracy Of Skin Cancer Detection Using CNN Algorithm

E. Madhankumar; G. Saranraj; S. Prabhu; Valarmathi Ramasamy

doi:10.2991/978-94-6463-858-5_254

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Enhancing The Predicition Accuracy Of Skin Cancer Detection Using CNN Algorithm

Authors

E. Madhankumar¹^{, *}, G. Saranraj¹, S. Prabhu¹, Valarmathi Ramasamy¹

¹Department of ECE, St.Peter’s College of Engineering and Technology, Avadi, India

^*Corresponding author. Email: madankumar@spcet.ac.in

Corresponding Author

E. Madhankumar

Available Online 4 November 2025.

DOI: 10.2991/978-94-6463-858-5_254 How to use a DOI?
Keywords: Skin Cancer Detection; Deep Learning; Convolutional Neural Networks (CNNs); Medical Image Classification; AI in Healthcare; Computer-Aided Diagnosis (CAD)
Abstract: Skin cancer remains one of the most common and life-threatening diseases globally, making early and accurate detection crucial for effective treatment. This study introduces a deep learning-based solution that significantly enhances skin cancer diagnosis. The proposed system employs a Convolutional Neural Network (CNN) to first classify skin lesion images as cancerous or non-cancerous. If cancerous, it further identifies the specific subtype, including melanoma, basal cell carcinoma, or squamous cell carcinoma. To boost model performance, techniques such as data augmentation, transfer learning, and hyperparameter tuning are utilized. Experimental results show superior accuracy, precision, and recall compared to traditional diagnostic methods. This research offers a scalable, AI-powered tool to support dermatologists in early detection and clinical decision-making, ultimately improving patient outcome.
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_254 How to use a DOI?
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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ris enw bib

TY  - CONF
AU  - E. Madhankumar
AU  - G. Saranraj
AU  - S. Prabhu
AU  - Valarmathi Ramasamy
PY  - 2025
DA  - 2025/11/04
TI  - Enhancing The Predicition Accuracy Of Skin Cancer Detection Using CNN Algorithm
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3037
EP  - 3051
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_254
DO  - 10.2991/978-94-6463-858-5_254
ID  - Madhankumar2025
ER  -

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