A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study
ABSTRACT Background and Aims White Blood Cells (WBCs) are essential for immune defense against infections. Automated WBC identification from microscopic images aids in diagnosing diseases like leukemia and AIDS. However, the complexity of WBC morphology due to varying maturation stages and staining...
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| Format: | Article |
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Wiley
2025-06-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70859 |
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| author | Md Shahin Ali Md. Maruf Hossain Md. Mahfuz Ahmed Kazi Rubaya Nowrin S. M. Mahim Shakib Al Hasan Moutushi Akter Kona Md Shafiqul Islam Kazi Mowdud Ahmed Md Mahbubur Rahman Md Khairul Islam |
| author_facet | Md Shahin Ali Md. Maruf Hossain Md. Mahfuz Ahmed Kazi Rubaya Nowrin S. M. Mahim Shakib Al Hasan Moutushi Akter Kona Md Shafiqul Islam Kazi Mowdud Ahmed Md Mahbubur Rahman Md Khairul Islam |
| author_sort | Md Shahin Ali |
| collection | DOAJ |
| description | ABSTRACT Background and Aims White Blood Cells (WBCs) are essential for immune defense against infections. Automated WBC identification from microscopic images aids in diagnosing diseases like leukemia and AIDS. However, the complexity of WBC morphology due to varying maturation stages and staining techniques complicates classification. This study aims to enhance WBC detection and classification using a hybrid VGG16‐Vision Transformer (VGG16‐ViT) model. Methods To enhance the efficiency of the classification process, preprocessing techniques such as data normalization, categorical variable encoding, feature extraction, and data augmentation were employed in conjunction with the proposed model before the training phase. The VGG16‐ViT model was trained and evaluated on two datasets to measure its performance. Results The overall success rate for classifying WBCs was 98.12% for Dataset 1% and 99.60% for Dataset 2. The measured average precision, recall, and F1‐score values were 98.59%, 98.23%, and 98.35% for Dataset 1; similarly, 98.95%, 99.98%, and 99.48% for Dataset 2. The experimental results indicated that the classification success was strengthened when the proposed model was combined with specific preprocessing procedures, outperforming existing research. Conclusion The hybrid VGG16‐ViT model, combined with effective preprocessing techniques, significantly improved the detection and classification of WBCs. Additionally, the training approach of the proposed model is less time‐consuming than existing transfer learning models, making it a valuable tool for assisting medical professionals in diagnosing diseases related to WBCs. |
| format | Article |
| id | doaj-art-6824ae44cc6248ed867dbd9739f6beb1 |
| institution | OA Journals |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-6824ae44cc6248ed867dbd9739f6beb12025-08-20T02:35:26ZengWileyHealth Science Reports2398-88352025-06-0186n/an/a10.1002/hsr2.70859A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective StudyMd Shahin Ali0Md. Maruf Hossain1Md. Mahfuz Ahmed2Kazi Rubaya Nowrin3S. M. Mahim4Shakib Al Hasan5Moutushi Akter Kona6Md Shafiqul Islam7Kazi Mowdud Ahmed8Md Mahbubur Rahman9Md Khairul Islam10Department of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshDepartment of Electrical and Electronic Engineering Islamic University Kushtia BangladeshDepartment of Information and Communication Technology Islamic University Kushtia BangladeshDepartment of Information and Communication Technology Islamic University Kushtia BangladeshDepartment of Biomedical Engineering Islamic University Kushtia BangladeshABSTRACT Background and Aims White Blood Cells (WBCs) are essential for immune defense against infections. Automated WBC identification from microscopic images aids in diagnosing diseases like leukemia and AIDS. However, the complexity of WBC morphology due to varying maturation stages and staining techniques complicates classification. This study aims to enhance WBC detection and classification using a hybrid VGG16‐Vision Transformer (VGG16‐ViT) model. Methods To enhance the efficiency of the classification process, preprocessing techniques such as data normalization, categorical variable encoding, feature extraction, and data augmentation were employed in conjunction with the proposed model before the training phase. The VGG16‐ViT model was trained and evaluated on two datasets to measure its performance. Results The overall success rate for classifying WBCs was 98.12% for Dataset 1% and 99.60% for Dataset 2. The measured average precision, recall, and F1‐score values were 98.59%, 98.23%, and 98.35% for Dataset 1; similarly, 98.95%, 99.98%, and 99.48% for Dataset 2. The experimental results indicated that the classification success was strengthened when the proposed model was combined with specific preprocessing procedures, outperforming existing research. Conclusion The hybrid VGG16‐ViT model, combined with effective preprocessing techniques, significantly improved the detection and classification of WBCs. Additionally, the training approach of the proposed model is less time‐consuming than existing transfer learning models, making it a valuable tool for assisting medical professionals in diagnosing diseases related to WBCs.https://doi.org/10.1002/hsr2.70859feature extractionGrad‐CAMmedical image processingVGG16‐ViTwhite blood cell |
| spellingShingle | Md Shahin Ali Md. Maruf Hossain Md. Mahfuz Ahmed Kazi Rubaya Nowrin S. M. Mahim Shakib Al Hasan Moutushi Akter Kona Md Shafiqul Islam Kazi Mowdud Ahmed Md Mahbubur Rahman Md Khairul Islam A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study Health Science Reports feature extraction Grad‐CAM medical image processing VGG16‐ViT white blood cell |
| title | A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study |
| title_full | A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study |
| title_fullStr | A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study |
| title_full_unstemmed | A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study |
| title_short | A Hybrid VGG16‐ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study |
| title_sort | hybrid vgg16 vit approach with image processing techniques for improved white blood cell classification and disease diagnosis a retrospective study |
| topic | feature extraction Grad‐CAM medical image processing VGG16‐ViT white blood cell |
| url | https://doi.org/10.1002/hsr2.70859 |
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