Abstract
In this article, Explainable Cubic Attention-based Autoencoder (ECAbA) model is proposed for skin cancer classification. The proposed model has a data augmentation module, which transform an imbalanced HAM10000 dataset into a refined and balanced form. In the second step, Cubic Attention-based Autoencoder (CAbA) model is proposed, which has 5 3D convolutional layers, 2 attention modules and one flatten, dense and neural successive cancellation layers. The trained model is then used for three explainable models, i.e., LIME, Grad-CAM, and Kernel SHAP, to make the explanations of predicted images. The outputs of these explainer modules make the predictions of CAbA model explainable and reliable. For fostering confidence among medical practitioners, the proposed model can assist in the adoption of AI approaches.
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Nasir, I.M., Tehsin, S., Damaševičius, R., Zielonka, A., Woźniak, M. (2025). Explainable Cubic Attention-Based Autoencoder for Skin Cancer Classification. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2024. Lecture Notes in Computer Science(), vol 15166. Springer, Cham. https://doi.org/10.1007/978-3-031-81596-6_11
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