Skin Cancer Detection Using Machine Learning and Dermoscopic Imaging

Skin cancer, such as melanoma, is a serious disease, but early detection can greatly improve treatment success.This paper presents a simple machine learning (ML) approach to detect skin cancer using dermoscopic images, which are close-up pictures of skin spots.We use a basic convolutional neural network (CNN), an ML tool good at analyzing images, to classify skin spots as benign (safe) or malignant (cancerous).Using the ISIC 2019 dataset and some computer-generated images, our CNN achieves 88% accuracy, outperforming a simple decision tree model.We explain the steps clearly, show results in tables, and provide Python code for a bar chart.We also discuss challenges, like needing more images, and suggest future improvements, like adding more data.This paper is designed for BTech students to learn how ML can help doctors detect skin cancer.