Skin Cancer Detection Using Machine Learning

Authors- Helan Sharmila M, Assistance Professor Dr.V.Sumalatha

Abstract-– Skin cancer is one of the most common types of cancer globally, and early detection plays a crucial role in improving patient outcomes. Traditional methods for diagnosing skin cancer involve visual examination by dermatologists, followed by biopsies for confirmation, which can be time-consuming and subject to human error. This study proposes a machine learning-based approach for the automatic detection of skin cancer from dermoscopic images. Using various machine learning algorithms, the model is trained on a large dataset of skin lesion images to distinguish between benign and malignant tumors. The proposed system utilizes feature extraction and classification techniques to detect melanoma and other skin cancers with high accuracy. This automated solution aims to assist dermatologists by providing a reliable and efficient tool for early skin cancer detection, reducing diagnostic errors and enabling timely intervention.

DOI: /10.61463/ijset.vol.13.issue2.403