A Novel Vision Transformer with Residual in Self-attention for Biomedical Image Classification

2023/06/02 by Arun Sharma, Sharma, Arun K., Nishchal K. Verma +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Brain Tumor Detection and Classification #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2306.01594

openalex publication_date 2023/06/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Biomedical image classification requires capturing of bio-informatics based on specific feature distribution. In most of such applications, there are mainly challenges due to limited availability of samples for diseased cases and imbalanced nature of dataset. This article presents the novel framework of multi-head self-attention for vision transformer (ViT) which makes capable of capturing the specific image features for classification and analysis. The proposed method uses the concept of residual connection for accumulating the best attention output in each block of multi-head attention. The proposed framework has been evaluated on two small datasets: (i) blood cell classification dataset and (ii) brain tumor detection using brain MRI images. The results show the significant improvement over traditional ViT and other convolution based state-of-the-art classification models.

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