Melanoma detection: integrating dilated convolutional methods with mutual learning-based artificial bee colony and reinforcement learning

This research tackles the problem of early melanoma detection, a critical concern due to the aggressive nature of the disease often initiated by deoxyribonucleic acid (DNA) damage from ultraviolet radiation. Early detection is vital for increasing survival rates, yet existing models face challenges like unbalanced classification and sensitivity to initial weight settings. Our study introduces an integrated method that enhances detection capabilities using an ensemble of CNNs (convolutional neural networks) to analyze complex image features, combined with a novel mutual learning-based artificial bee colony (ML-ABC) algorithm for optimizing initial weight configurations. Furthermore, we employ a reinforcement learning approach to address data imbalance effectively. The ML-ABC algorithm uses a mutual learning strategy that adjusts potential food source positions based on the superior fitness of two selected individuals through a mutual learning factor. Our model conceptualizes the classification problem as a sequence of decision-making steps where each classification action is rewarded, with more rewards for the minority class to mitigate imbalance. Our approach was tested on the ISIC-2020 dataset and outperformed existing methods, achieving high accuracy (0.8661) and F-measure (0.8540). The proposed model represents a significant advancement in melanoma screening technologies, promising to elevate the success rates of early interventions.