Inverse Gini indexed averaging: A multi-leveled ensemble approach for skin lesion classification using attention-integrated customized ResNet variants.

Objective: To improve the accuracy and explainability of skin lesion detection and classification, particularly for several types of skin cancers, through a novel approach based on the convolutional neural networks with attention-integrated customized ResNet variants (CRVs) and an optimized ensemble learning (EL) strategy. Methods: Our approach utilizes all ResNet variants combined with three attention mechanisms: channel attention, soft attention, and squeeze-excitation attention. These attention-integrated ResNet variants are aggregated through a unique multi-level EL strategy. We propose an innovative weight optimization method, inverse Gini indexed averaging (IGIA), which is further extended to multi-leveled IGIA (ML-IGIA) to determine the optimal weights for each model within multiple ensemble levels. For interpretability, we employ gradient class activation map to highlight the regions responsible for classification dominance, enhancing the model's transparency. Results: Our method was evaluated on the Human Against Machines 10000 dataset, achieving a superior accuracy of 94.52% with the ML-IGIA approach, outperforming existing methods. Conclusions: The proposed CRV-based ensemble model with ML-IGIA demonstrates robust performance in skin lesion classification, offering both high accuracy and enhanced interpretability. This approach addresses the current research gap in effective weight optimization in EL and supports timely, automated skin disease detection.