An Explainable Deep Learning Framework for Multi-Class Skin Lesion Classification While Resolving Class Imbalance

Skin lesions are indicative of various skin diseases, including serious conditions like melanoma, which can be life-threatening if not detected early. Automated systems for analyzing dermatoscopic images have shown promise in improving diagnostic accuracy. However, many existing studies rely on pre-trained convolutional neural network (CNN) architectures without customization, leading to potential biases and suboptimal performance. In this study, we propose a customized DenseNet121 architecture tailored for skin lesion classification. We address class imbalance issues through data augmentation and fine-tune the model to extract fine-grained features. Our method surpasses other cutting-edge models in terms of accuracy, precision, recall, specificity, sensitivity, and root mean square (ROC) values. Furthermore, we utilise Gradient-weighted Class Activation Mapping (Grad-CAM) to visually represent the decision-making process of the model. The next study will prioritise the validation of the model using a wide range of datasets and the integration of lesion-related information to enhance its performance.