Shifted Window Vision Transformer for Blood Cell Classification
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 advan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/11/2442 |
| _version_ | 1797597649369563136 |
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| author | Shuwen Chen Siyuan Lu Shuihua Wang Yiyang Ni Yudong Zhang |
| author_facet | Shuwen Chen Siyuan Lu Shuihua Wang Yiyang Ni Yudong Zhang |
| author_sort | Shuwen Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:08:33Z |
| format | Article |
| id | doaj.art-5afba523c2944a3882924d5a8f295840 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:08:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-5afba523c2944a3882924d5a8f2958402023-11-18T07:45:00ZengMDPI AGElectronics2079-92922023-05-011211244210.3390/electronics12112442Shifted Window Vision Transformer for Blood Cell ClassificationShuwen Chen0Siyuan Lu1Shuihua Wang2Yiyang Ni3Yudong Zhang4School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKSchool of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing 210013, ChinaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKBlood 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.https://www.mdpi.com/2079-9292/12/11/2442blood cellcomputer-aided diagnosiscomputer visiondeep learningvision transformer |
| spellingShingle | Shuwen Chen Siyuan Lu Shuihua Wang Yiyang Ni Yudong Zhang Shifted Window Vision Transformer for Blood Cell Classification Electronics blood cell computer-aided diagnosis computer vision deep learning vision transformer |
| title | Shifted Window Vision Transformer for Blood Cell Classification |
| title_full | Shifted Window Vision Transformer for Blood Cell Classification |
| title_fullStr | Shifted Window Vision Transformer for Blood Cell Classification |
| title_full_unstemmed | Shifted Window Vision Transformer for Blood Cell Classification |
| title_short | Shifted Window Vision Transformer for Blood Cell Classification |
| title_sort | shifted window vision transformer for blood cell classification |
| topic | blood cell computer-aided diagnosis computer vision deep learning vision transformer |
| url | https://www.mdpi.com/2079-9292/12/11/2442 |
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