Utilizing Genetic Feature Augmentation and CAM Consistency for Enhanced Classification of Imbalanced Skin Lesion Data

Automated classification of skin lesions has been proven to improve the diagnostic accuracy of dermoscopic images significantly. Despite numerous achievements in this field, accurate classification remains challenging due to the diversity of skin cancer lesions and the class imbalance within datasets. Traditional methods typically augment imbalanced datasets by increasing perturbations to input data, but the improvements are often limited. Therefore, this paper proposes a method that utilizes genetic feature augmentation and class activation mapping (CAM) consistency for enhanced classification of imbalanced skin lesion data. The proposed genetic feature module incorporates the concept of genetic algorithms into the network model. The model can learn more diverse expressions by selectively retaining highly representative features while cross-replicating data from underrepresented classes. Additionally, we introduce a global lesion localization module based on CAM to enhance the learning of discriminative features among classes. This module optimizes the multiple CAM distances generated from the same dermoscopic image, thereby improving the differentiation of inter-class features. To validate the effectiveness of this method, extensive experiments were conducted on the ISIC-2017 and ISIC-2018 datasets. The experimental results demonstrate that this method performs exceptionally well on multi-class data, significantly improving classification accuracy and stability.