Leveraging CNN Architectures for Accurate and Scalable Skin Cancer Detection

This paper presents a study on an automated skin cancer detection system using deep learning techniques. By leveraging convolutional neural networks (CNNs), the model analyzes dermoscopic images to classify skin lesions as benign or malignant. Through image preprocessing, augmentation, and rigorous evaluation, the system achieves high accuracy, sensitivity, and specificity. This research demonstrates the potential of AI-assisted dermatology tools for early diagnosis and improved patient outcomes, contributing to medical image analysis and clinical applications.