E-MedViTR: Enhanced Vision Transformers with Registers for Biomedical Image Classification

Medical image classification is critical in clinical decision-making, requiring efficient and precise evaluation of images. Convolutional Neural Networks (CNNs) have significantly contributed to this field, yet their resource-intensive nature poses challenges. This study aims to analyse the effectiveness of vision transformers with registers for medical image classification on the MedMNIST dataset, comparing them with other state-of-the-art classifiers. We propose an E-MedViTR model that boosts the performance of the vision transformer with registers by adding an enhanced classifier head. Our suggested model surpasses existing counterparts, achieving the highest F-1 scores in multi-class classification, demonstrating its effectiveness in colon pathology image classification. The model scores an accuracy of 85.80% on the PathMNIST, one of the sub-datasets of the MedMNIST dataset. Research in this field could pave the way for more personalized, accurate, and efficient diagnostic procedures in medical imaging, benefiting both clinicians and patients.