A Hybrid Deep Learning Model Combining VGG19 and AD_Net through Feature-Level Fusion for Real-Time Skin Cancer Classification

Ali Atshan Abdulredah^1,2,* , Monji Kherallah³, Faiza Charfi³

‎¹National School of Electronics and Telecoms of Sfax, University of Sfax, Tunisia

‎²College of Computer Science and information Technology, University of Sumer, Iraq

‎ ³Faculty of Science of Sfax, University of Sfax, Tunisia

Emails: ali.atshan@uos.edu.iq; Monji.Kherallah@gmail.com; Faiza.charfi@gmail.com

Abstract

Automated detection (AD) techniques are essential for early recognition of skin cancer. Hybrid models using feature fusion, which combine pre-trained CNNs with customized models, have shown superiority in real-time skin cancer pathology classification. This study combines VGG19 feature maps with a novel learning network based framework called AD_Net to enhance classification accuracy. VGG19 facilitated robust low-level feature extraction, while AD_Net brilliantly extracts specialized patterns. This strategy provided a flexible and fast architecture, suitable for real-time medical applications. This work led to the classification of three of the most lethal skin cancer types. The model was trained and validated using experiments on the publicly available ISIC2019 dataset. In order to improve the interpretability of the model's predictions, interpretable artificial intelligence (XAI) techniques particularly Grad-CAM were applied. Four baseline models EfficientNetB0, MobileNetV2, Inception-v3, and VGG16, were used to assess the proposal's efficacy. The suggested model outperformed the four baseline models with 99.18% accuracy, 99.0% precision, 99.0% recall, and 99.0% F1 score. Dermatologists and other medical professionals can use this method to detect skin cancer early.

Keywords: Hybrid models; Feature fusion; Skin cancer; Explainable AI (XAI); Automated ‎diagnosis (AD_Net)