Dual concatenated transfer learning with attention fusion: An ensemble-enhanced approach for skin lesion classification.

Objective: Classification of skin lesions plays a crucial role in the early detection and diagnosis of various dermatological conditions. The existing deep learning models are plagued by class imbalance, bad feature extraction, and generalization to unseen data. This study aims to develop a robust hybrid deep learning model for multi-class skin lesion classification. Methods: We propose a hybrid architecture combining three DenseNet models (DN121, DN169, DN201) and three ResNet models (RN50, RN101, RN152) with attention mechanisms (channel attention, squeeze-and-excitation, soft attention). We concatenated the architectures in a dual way. Finally, the concatenated models are ensembled to enhance performance. The model is trained and evaluated on the HAM10000 dataset, with advanced augmentation strategies applied to address class imbalance and improve generalization on unseen data. Results: The model achieves an accuracy rate of 91.43% and specificity of 92.04%, bettering existing baseline methods. Attention mechanisms significantly improve feature extraction, dual concatenation provides better feature fusion, and ensemble integration enhances overall model robustness. Conclusion: Our attention mechanism-based hybrid architecture is a robust and reliable solution for machine-based skin lesion classification. Its strong performance indicates its potential to help dermatologists with timely, precise diagnosis, serving as a foundation for other innovations in medical image analysis.