A novel deep semantic- and vision-based self-attention architecture for skin cancer classification.

Objectives: In the world, skin cancer is a significant health concern, and early diagnosis of this cancer plays a key role in improving patient outcomes. The early detection of this cancer reduces the death rate, but due to the complexity of the diagnosis, incorrect detection and prediction are provided by the experts. Therefore, it is essential to propose a computer-aided diagnostic system based on deep learning and explainable Artificial Intelligence (XAI) techniques that can be used as a second opinion in clinics and help physicians more accurately detect and predict this type of cancer. Methods: This work presents the proposed deep learning architecture consisting of two modules-skin lesion segmentation and lesion type classification. The proposed architecture is interpreted using XAI techniques to better evaluate the black-box model. In the skin lesion segmentation phase, we implemented DeepLab V3 architecture for semantic segmentation. The ResNet-18 model was used as the backbone, and later hyperparameters were optimized using Bayesian Optimization (BO). In the classification phase, we design a FusedNet architecture called Inverted self-attention with Vision Transformer (ISAwViT). The proposed fused network combines an inverted self-attention residual architecture with a vision transformer. The proposed fused network extracted feature information more deeply than performing an accurate prediction in a later stage. The design model is trained, and later in the testing phase, extracted features are classified using Softmax and several other classifiers. Results: The lesion segmentation and classification experiment was conducted on the HAM10000 dataset. The accuracy achieved by the HAM10000 dataset was 95.16% for lesion segmentation and 97.5% for lesion classification. Conclusion: Compared with recent techniques, the proposed model is more effective and efficient. In addition, the interpretation of the proposed model was performed using LIME and Grad-CAM, which show how the fused model makes correct classifications.