Shifted Window Vision Transformer for Blood Cell Classification

Shuwen Chen, Siyuan Lu, Shuihua Wang, Yiyang Ni, Yudong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Blood cells play an important role in the metabolism of the human body, and the status of blood cells can be used for clinical diagnoses, such as the ratio of different blood cells. Therefore, blood cell classification is a primary task, which requires much time for manual analysis. The recent advances in computer vision can be beneficial to free doctors from tedious tasks. In this paper, a novel automated blood cell classification model based on the shifted window vision transformer (SW-ViT) is proposed. The SW-ViT architecture is firstly pre-trained on the ImageNet dataset and fine-tuned on the blood cell images for classification. Two transfer strategies are employed to generate better classification results. One is to fine-tune the entire SW-ViT, and the other is to only fine-tune the linear output layer of the SW-ViT while all the other parameters are frozen. A public dataset named BCCD_Dataset (Blood Cell Count and Detection) is utilized in the experiments. The results show that the SW-ViT outperforms several state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis.

Original languageEnglish
Article number2442
JournalElectronics (Switzerland)
Volume12
Issue number11
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Keywords

  • blood cell
  • computer vision
  • computer-aided diagnosis
  • deep learning
  • vision transformer

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