A Hybrid Deep Learning Model to Improve Skin Cancer Classification

A Hybrid Deep Learning Model to Improve Skin Cancer Classification

Harsh Baliyan

Department of CSE, Gautam Budhha University,

Greater Noida, Uttar Pradesh, India baliyanh625@gmail.com

Pradeep Tomar

Department of CSE, Gautam Buddha University,

Greater Noida, Uttar Pradesh, India pradeep.tomar@gbu.ac.in

Saumya Dixit

Department of Computer Science, University of Texas at Dallas, Richardson, Texas, USA saumya.dixit@yahoo.com

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

I.    Introduction