An Interpretable Deep Learning Model for Skin Lesion Classification

Abstract

Skin Cancer is a dangerous issue in society. Early diagnosis and therapy are two of the most crucial steps in preventing the onset of a disease. Dermatologists primarily use visual methods to identify the skin lesions which may cause skin cancer. With the development of technology, methods for classifying skin lesions, like deep learning and computer vision, are gaining popularity. A hybrid model is proposed using a pre-trained DenseNet architecture integrated with Convolutional Block Attention Module (CBAM) for feature refinement. The HAM10000 dataset, which includes 10015 dermoscopic pictures with seven distinct skin disease types, was used in our research. The proposed approach outperforms the original pre-trained DenseNet models, with an average accuracy of 93%. The LIME framework is used to assess predictions further and produce visual explanations, which can assist in decreasing the drawback of black-box models in aiding medical decision-making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.