A Hybrid Deep Learning Model to Improve Skin Cancer Classification

Abstract—Among the various types of cancer globally, skin cancer is one of the most common, and identifying it early is vital for improving patient outcomes. This paper presents a novel hybrid deep learning approach for categorizing skin lesions into seven distinct groups using the HAM10000 dataset. We propose and compare two architectures: a CNN-DenseNet121 hybrid model and a CNN-ResNet50V2 hybrid model with an attention mechanism. Our experimental results demonstrate that the CNN-DenseNet121 model achieves superior overall accuracy, while the attention-based model shows improved focus on diagnostically relevant regions and better performance on certain challenging categories. The integration of attention mechanisms with transfer learning provides a more focused feature extraction process, which is essential for distinguishing subtle differences between benign and malignant skin conditions. This research contributes to the ongoing development of computer-aided diagnosis systems for dermatological applications. Index Terms—Neural networks, deep learning, attention models, dermatological cancer, classification of medical images, knowledge transfer