SKIN CANCER DETECTION USING CNN

Abstract

Skin cancer is one of the most common and lifethreatening diseases worldwide, where early detection plays a crucial role in improving survival rates. This project presents an intelligent system for skin cancer detection using Convolutional Neural Networks (CNN), a powerful deep learning technique for image classification. The proposed system aims to automate the diagnosis process by analyzing dermoscopic images of skin lesions and classifying them into benign or malignant categories. The system reduces reliance on manual examination by dermatologists, which is often time-consuming and prone to human error. The model is trained using a dataset of labeled skin lesion images, enabling it to learn complex patterns such as color variation, texture, and shape. Preprocessing techniques like resizing, normalization, and noise removal enhance the quality of images before feeding them into the model. Feature extraction is automatically handled by CNN layers, improving classification accuracy. The system is designed to be user-friendly, allowing users to upload images and receive instant predictions. Experimental results demonstrate that the CNN-based approach achieves high accuracy compared to traditional machine learning methods. The proposed solution provides a fast, costeffective, and reliable diagnostic tool, assisting healthcare professionals in early detection and treatment planning. This system highlights the importance of artificial intelligence in modern healthcare and its potential to revolutionize medical diagnostics by providing scalable and efficient solutions.