SELF-RELIANT RESIDUAL NETWORK BASED DEEP LEARNING FRAMEWORK FOR MELANOMA SKIN DISEASE DETECTION

Melanoma is one of the deadliest types of skin cancer and one of the most aggressive that may be if caught late. While traditional approaches may have their limits, an accurate diagnosis is vital for patient survival. Thanks to its capacity to understand intricate patterns from massive datasets, deep learning has evolved as a potential method for automated melanoma diagnosis. The challenges persist, including overfitting, instability during training, and difficulties in handling nonlinearities, which can hinder accurate predictions. To address these challenges, Self-Reliant ResNet (SR-ResNet) has been proposed. This enhanced version of ResNet integrates Zoutendijk’s Method, a nonlinear optimization technique, to optimize weight updates and improve convergence. SR-ResNet features a series of residual blocks where Zoutendijk’s Method refines the learning process, ensuring stability and efficient training, even in deeper networks. The network’s architecture has been designed to enhance performance and generalization. The proposed SR-ResNet has been evaluated using a dataset of 10,000 Melanoma Skin Cancer images. The results demonstrate significant improvements in classification accuracy, achieving a high precision rate with reduced overfitting. SR-ResNet outperforms traditional models, establishing itself as a robust tool for melanoma diagnosis.