SKIN CANCER DETECTION

Skin cancer, particularly melanoma, remains a significant global health challenge, with high mortality rates often linked to delayed diagnosis.The subjective nature and dependency on expert experience in traditional visual diagnosis by dermatologists necessitate the development of objective and efficient computer-aided diagnostic (CAD) tools for early detection.This research presents an automated system for skin lesion classification using advanced Deep Learning (DL) techniques and Image Processing.The methodology involves: (1) acquiring and preprocessing dermoscopic images (e.g., noise reduction and normalization); (2) employing segmentation methods to accurately isolate the lesion boundary; and (3) utilizing a robust Convolutional Neural Network (CNN) architecture for automatic feature extraction and classification.The CNN model is trained to distinguish between benign and malignant skin lesions.Performance evaluation metrics, including accuracy, sensitivity, and specificity, demonstrate the system's effectiveness.The results show that the developed DL model achieves high diagnostic accuracy ($>90\%$), validating its potential as a powerful, non-invasive secondary screening tool.In conclusion, this project successfully leverages Deep Learning to create a reliable and scalable system that can assist medical professionals in the rapid and precise early detection of skin cancer, ultimately contributing to improved prognostic outcomes for patien