Kidney CT Scan Image Classification Using Modified Vision Transformer

Authors

  • Roshan Subedi Department of Electronics and Computer Engineering, IOE, Pashchimanchal Campus, Tribhuvan University, Nepal
  • Suresh Timilsina Department of Electronics and Computer Engineering, IOE, Pashchimanchal Campus, Tribhuvan University, Nepal
  • Smita Adhikari Department of Electronics and Computer Engineering, IOE, Pashchimanchal Campus, Tribhuvan University, Nepal

DOI:

https://doi.org/10.3126/jes2.v2i1.60381

Keywords:

CNN, Classification, CT, MLP, Vision Transformer

Abstract

With the rising number of kidney-related health issues, early and precise diagnosis is crucial. The study aims to create a reliable method for categorizing kidney CT scan images into four groups: Cyst, Normal, Tumor, and stone. Traditional approaches usually rely on typical Machine Learning (ML) and Convolution Neural Networks (CNNs). However, in this research, the potential of a novel model called Vision Transformer (ViT) is explored. ViT was initially designed for Natural Language Processing (NLP) tasks but shows promise for medical image classification. ViT’s capabilities are enhanced by coupling it with Fully Connected Networks (FCN). This combination helps to merge the feature extraction capability of the ViT and the classification ability of the FCN, which ultimately helps to overcome the challenge of detecting kidney-related issues.

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Published

2023-12-06

How to Cite

Subedi, R., Timilsina, S., & Adhikari, S. (2023). Kidney CT Scan Image Classification Using Modified Vision Transformer. Journal of Engineering and Sciences, 2(1), 24–29. https://doi.org/10.3126/jes2.v2i1.60381

Issue

Section

Articles