Medtransnet: advanced gating transformer network for medical image classification

Abstract

Accurate medical image classification poses a significant challenge in designing expert computer-aided diagnosis systems. While deep learning approaches have shown remarkable advancements over traditional techniques, addressing inter-class similarity and intra-class dissimilarity across medical imaging modalities remains challenging. This work introduces the advanced gating transformer network (MedTransNet), a deep learning model tailored for precise medical image classification. MedTransNet utilizes channel and multi-gate attention mechanisms, coupled with residual interconnections, to learn category-specific attention representations from diverse medical imaging modalities. Additionally, the use of gradient centralization during training helps in preventing overfitting and improving generalization, which is especially important in medical imaging applications where the availability of labeled data is often limited. Evaluation on benchmark datasets, including APTOS-2019, Figshare, and SARS-CoV-2, demonstrates effectiveness of the proposed MedTransNet across tasks such as diabetic retinopathy severity grading, multi-class brain tumor classification, and COVID-19 detection. Experimental results showcase MedTransNet achieving 85.68% accuracy for retinopathy grading, 98.37% (\(\pm \,0.44\)) for tumor classification, and 99.60% for COVID-19 detection, surpassing recent deep learning models. MedTransNet holds promise for significantly improving medical image classification accuracy.