Advanced Skin Cancer Classification Using Yet Another Hill Climbing

This paper introduces a novel hill-climbing technique aimed at maximizing the prediction accuracy in skin cancer classification. It highlights the efficacy of epoch–baseline termination management coupled with data augmentation, especially when dealing with the severely imbalanced HAM10000 data set. The method alters the hill-climbing function’s termination condition, enabling the system to evade local minima and find superior solutions. Termination conditions using baseline and epochs significantly enhance prediction accuracy, while data augmentation balances the data set. The method achieves a prediction accuracy of over 0.99 in classifying seven types of skin cancers using the HAM10000 data set, as validated by repetitive random cross-validation and the confusion matrix. The paper concludes that the proposed method, when combined with data augmentation, can enhance deep learning and is applicable to oncology classification. The advanced skin classification algorithm proposed herein has achieved the highest prediction accuracy in the benchmark with the HAM10000 data set, which can be used for cancer research classification in general.