Melanoma Skin Classification Using the Hybrid Approach Residual Network-Vision Transformer for Cancer Diagnosis.

Abstract

Introduction: Computer-aided diagnosis using deep neural networks allows for the analysis and processing of images or videos of different pathologies, providing valuable reference data to physicians for the diagnosis or screening of conditions such as skin cancer.

Methods: In this work, we highlight the contribution of Convolutional Neural Networks, pre-trained models, and Vision Transformer architectures in the classification of skin melanoma. The experimental aspect will therefore involve the contribution of the classical CNN, as well as models inspired by this CNN, namely, Inception V3, ResNet 50, AlexNet, and EfficientNet in addition to the hybrid architecture.

Results: The conducted experiments entailed the adjustment of multiple hyperparameters, leading to the development of an architecture that achieved optimal results. Additionally, employing a hybrid architecture not only facilitated the amalgamation of the strengths from two models (the top performing pretrained ResNet50 model with the Vision Transformer) but also led to enhanced accuracy. After training the dataset, the proposed models have contributed to progressively improving the results, eventually achieving a classification rate of 95.53% for the hybrid ResNet50-ViT model.

Conclusion: The aim of this research is to equip clinicians with a robust tool for melanoma diagnosis by leveraging the strengths of two models within the ResNet50-ViT hybrid framework.