Skin Lesion Detection and Classification using Convolutional Neural Network

Skin cancer, particularly melanoma, poses a major public health challenge due to its high mortality rate when not detected at an early stage. Computer-aided diagnosis (CAD) systems have emerged as valuable tools to assist dermatologists in accurately diagnosing skin lesions. This study introduces a system based on convolutional neural networks (CNNs) for detecting and classifying skin lesions. The proposed framework incorporates preprocessing techniques, such as image enhancement and noise reduction, to improve the clarity and visibility of lesions. This is followed by deep learning-based feature extraction and classification. The CNN model is trained using publicly available datasets like HAM10000, which provides a diverse collection of dermoscopic images representing various types of lesions. The system demonstrates high accuracy in differentiating malignant from benign lesions, underscoring the potential of deep learning in medical image analysis. By facilitating early detection, this approach seeks to serve as a reliable and efficient tool, improving treatment outcomes and enhancing patient survival rates..