Explainable Deep Learning for Automated Skin Cancer Detection Using Advanced CNN Architectures on Dermoscopic Images

Skin cancer is a considerable health issue worldwide, occurring when pigment cells turn malignant. However, diagnosing skin lesions is difficult for dermatologists because most lesions have similar characteristics. Initial detection is essential because it significantly increases the success rate of treatment and survival rates. In the past few decades, the rapid development of artificial intelligence has made it possible to build automated diagnostic systems based on large histopathology-validated image datasets. In this study, we introduce a deep learning solution for multi-class skin cancer classification based on state-of-the-art convolutional neural networks (CNNs) on the HAM10000+ISC image dataset. We used pre-trained CNN backbones, InceptionV3, DenseNet121, ResNet50, and VGG16, initialized with weights from ImageNet, for feature extraction, fine-tuning, and evaluation. Among the models, InceptionV3 achieved the highest accuracy of 76% and an ROC score of 0.967. To enhance interpretability, we used explainable AI (XAI) methods, Grad-CAM, Grad-CAM++, and class-wise attention maps, to examine both correctly and incorrectly classified images. The experiment demonstrates that the suggested system is not only characterized by high classification accuracy, but also by the ability to explain and visualize, which is a significant advantage for dermatologists when diagnosing skin cancer early and correctly.