Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/12/2903 |
| _version_ | 1797460676924407808 |
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| author | Thinam Tamang Sushish Baral May Phu Paing |
| author_facet | Thinam Tamang Sushish Baral May Phu Paing |
| author_sort | Thinam Tamang |
| collection | DOAJ |
| description | White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance. |
| first_indexed | 2024-03-09T17:09:26Z |
| format | Article |
| id | doaj.art-81d2d9168b8c48c8bef0fbeba198f0f2 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T17:09:26Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-81d2d9168b8c48c8bef0fbeba198f0f22023-11-24T14:15:14ZengMDPI AGDiagnostics2075-44182022-11-011212290310.3390/diagnostics12122903Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural NetworksThinam Tamang0Sushish Baral1May Phu Paing2Madan Bhandari Memorial College, New Baneshwor, Kathmandu 44600, NepalDepartment of Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Biomedical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, ThailandWhite blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.https://www.mdpi.com/2075-4418/12/12/2903complete blood clountdeep learninglabel smoothingmixup augmentationnormalization |
| spellingShingle | Thinam Tamang Sushish Baral May Phu Paing Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks Diagnostics complete blood clount deep learning label smoothing mixup augmentation normalization |
| title | Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks |
| title_full | Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks |
| title_fullStr | Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks |
| title_full_unstemmed | Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks |
| title_short | Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks |
| title_sort | classification of white blood cells a comprehensive study using transfer learning based on convolutional neural networks |
| topic | complete blood clount deep learning label smoothing mixup augmentation normalization |
| url | https://www.mdpi.com/2075-4418/12/12/2903 |
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