Automated Skin Lesion Classification Using Machine Learning: A Comparative Study of Model Performance

DOI

10.2991/978-94-6463-866-0_65How to use a DOI?

Keywords

Skin Cancer; Basal Cell Carcinoma (BCC); Squamous Cell Carcinoma (SCC); Melanoma

Abstract

Skin lesion detection using machine learning has emerged as a crucial tool in early diagnosis and treatment planning for skin cancer, particularly melanoma. This research focuses on implementing multiple machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and Decision Tree, to classify and compare the accuracy of skin lesion detection models. By leveraging datasets from Kaggle and utilizing a full-stack web application, the system provides an intuitive interface for users to upload skin lesion images and receive diagnostic predictions. The proposed methodology integrates data preprocessing, feature extraction, and model training to optimize classification performance. This interactive component aims to improve public awareness and encourage early intervention. The web application is developed using a full-stack approach, ensuring seamless user experience and real-time data processing. Through comprehensive testing, the research evaluates model accuracy, sensitivity, and specificity to determine the most effective approach for skin lesion classification. By integrating machine learning with a user-friendly web interface, this study contributes to enhancing early detection and prevention strategies for melanoma, ultimately aiding in reducing the global burden of skin cancer.

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  - Vaibhav Sahu
AU  - Arpita Yadav
AU  - Niharika Kumari
AU  - S. Amutha
PY  - 2025
DA  - 2025/10/31
TI  - Automated Skin Lesion Classification Using Machine Learning: A Comparative Study of Model Performance
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 800
EP  - 811
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_65
DO  - 10.2991/978-94-6463-866-0_65
ID  - Sahu2025
ER  -