Automatic Classification of White Blood Cells Using Pre-Trained Deep Models
White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based comp...
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| Format: | Article |
| Language: | English |
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Sakarya University
2022-12-01
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| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/2740488 |
| _version_ | 1797351864401920000 |
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| author | Oğuzhan Katar İlhan Fırat Kılınçer |
| author_facet | Oğuzhan Katar İlhan Fırat Kılınçer |
| author_sort | Oğuzhan Katar |
| collection | DOAJ |
| description | White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs. |
| first_indexed | 2024-03-08T13:06:42Z |
| format | Article |
| id | doaj.art-f38243f8e25540b1a3dbd5e13badce8c |
| institution | Directory Open Access Journal |
| issn | 2636-8129 |
| language | English |
| last_indexed | 2024-03-08T13:06:42Z |
| publishDate | 2022-12-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj.art-f38243f8e25540b1a3dbd5e13badce8c2024-01-18T16:44:35ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292022-12-015346247610.35377/saucis...119693428Automatic Classification of White Blood Cells Using Pre-Trained Deep ModelsOğuzhan Katar0İlhan Fırat Kılınçer1FIRAT ÜNİVERSİTESİFIRAT ÜNİVERSİTESİWhite blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.https://dergipark.org.tr/tr/download/article-file/2740488white blood cellsclassificationpre-trained modelsartificial intelligence |
| spellingShingle | Oğuzhan Katar İlhan Fırat Kılınçer Automatic Classification of White Blood Cells Using Pre-Trained Deep Models Sakarya University Journal of Computer and Information Sciences white blood cells classification pre-trained models artificial intelligence |
| title | Automatic Classification of White Blood Cells Using Pre-Trained Deep Models |
| title_full | Automatic Classification of White Blood Cells Using Pre-Trained Deep Models |
| title_fullStr | Automatic Classification of White Blood Cells Using Pre-Trained Deep Models |
| title_full_unstemmed | Automatic Classification of White Blood Cells Using Pre-Trained Deep Models |
| title_short | Automatic Classification of White Blood Cells Using Pre-Trained Deep Models |
| title_sort | automatic classification of white blood cells using pre trained deep models |
| topic | white blood cells classification pre-trained models artificial intelligence |
| url | https://dergipark.org.tr/tr/download/article-file/2740488 |
| work_keys_str_mv | AT oguzhankatar automaticclassificationofwhitebloodcellsusingpretraineddeepmodels AT ilhanfıratkılıncer automaticclassificationofwhitebloodcellsusingpretraineddeepmodels |