Abstract

Early and accurate detection of melanoma remains a critical challenge in medical imaging. Convolutional Neural Networks (CNNs) have demonstrated superior classification performance, often surpassing dermatologists in diagnostic accuracy. However, optimizing CNN architectures for clinical applications requires careful consideration of performance metrics that reflect both overall accuracy and Recall (sensitivity) to malignant cases. This article explores 45 recent research contributions addressing melanoma classification, offering a critical overview of current trends and techniques. Based on these findings, we propose a novel weighted multi-objective function (MOF) that balances key performance metrics according to clinical needs, with particular emphasis on Accuracy for overall correctness, Recall for reducing false negatives, and AUC for enhancing class discrimination in melanoma detection, thus ensuring both general reliability and high sensitivity in detecting melanoma. The Grey Wolf Optimizer (GWO), a population-based metaheuristic known for its effective exploration of high-dimensional search spaces, is employed for CNN hyperparameter tuning. Experimental results demonstrate that the proposed approach enhances model performance across key clinical metrics, obtaining a MOF score of 0.9032, with Accuracy, Recall, and AUC values of 0.9025, 0.9101 and 0.8925, respectively. This confirms the effectiveness of the strategy in achieving both high diagnostic accuracy and sensitivity, offering a robust and scalable strategy for medical image classification in computer-aided diagnosis systems.