Skin Cancer Detection and Classification for Melanoma Via Optimized Deep CNN Architecture

Skin cancer melanoma presents a significant challenge in dermatology due to its aggressive nature and increasing mortality rates. Accurate and timely classification of melanoma severity is crucial for effective treatment and improved patient outcomes. Melanoma is a highly aggressive form of skin cancer, requires accurate and early detection to improve patient outcomes. This study introduces a novel classification model designed to assess the severity of melanoma using advanced deep learning techniques. The model leverages a comprehensive dataset comprising clinical, histopathological, and imaging data to train and validate its predictive capabilities. This proposed model is evaluated for their performance in distinguishing between different stages of melanoma. This paper explores the use of a ResNet-50 model for classifying skin cancer melanoma, prioritizing high recall for early detection. The model achieved 91% accuracy, 88.5% precision, and 92% recall, effectively identifying most melanoma cases. Training and validation metrics revealed steady performance improvements, with minor overfitting observed. According to the results, the ResNet-50 model is a viable method for accurately diagnosing melanoma, and next research should focus on improving precision without sacrificing high recall.