Optimized Deep Learning Architecture for Melanoma Detection

Melanoma is a highly aggressive form of skin cancer that greatly impacts the global mortality rate related to skin cancer. Accurate identification and precise assessment of illness severity are essential for improving patient outcomes. The automatic classification of skin lesions by imaging is challenging due to the complex differences in their visual characteristics. This study employs deep learning algorithms to identify and distinguish between benign and malignant melanoma skin cancer. The malignancy is classified into seven distinct types: melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The preliminary step of the proposed system entails a preprocessing stage where normalization and data augmentation techniques are utilized to prepare the HAM10000 dataset for the classification of benign and malignant cancer lesions. This research proposes a novel variant of the Residual Neural Network (ResNet), namely ResNet50V2.5, for enhanced picture categorization, optimizing training efficiency by circumventing unnecessary layers and improving model performance. A comparative investigation of five designs, including ResNet101, ResNet101V2, ResNet50, ResNet50V2, and ResNet50V2.5, demonstrates that the ResNet50V2.5 model attains superior accuracy, achieving a classification performance of 99.17%, thereby surpassing existing architectures in skin cancer diagnosis.