Deep learning-Based Skin Cancer Identification with CNN

Skin cancer is one of the most common and rapidly increasing forms of cancer worldwide, necessitating early and accurate detection for effective treatment. Traditional diagnostic methods rely heavily on visual examination and biopsy, which can be time-consuming, subjective, and costly. Recent advancements in deep learning have shown promising results in automating skin cancer classification, improving diagnostic accuracy, and reducing dependency on clinical expertise. This study proposes a Convolutional Neural Network (CNN)-based skin classification model for skin cancer identification. The proposed model is trained on a large dataset of DERMOSCOPIC images, enabling it to automatically extract relevant features and differentiate between malignant and benign skin lesions. The CNN architecture enhances feature learning through multiple convolutional layers, improving classification accuracy compared to conventional machine learning techniques. Experimental evaluations demonstrate that the proposed model achieves high accuracy, sensitivity, and specificity, making it a reliable tool for assisting dermatologists in early skin cancer detection.