Complete Blood Cell Detection and Counting Based on Deep Neural Networks
Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep le...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8140 |
| _version_ | 1797411339870666752 |
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| author | Shin-Jye Lee Pei-Yun Chen Jeng-Wei Lin |
| author_facet | Shin-Jye Lee Pei-Yun Chen Jeng-Wei Lin |
| author_sort | Shin-Jye Lee |
| collection | DOAJ |
| description | Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping. |
| first_indexed | 2024-03-09T04:43:42Z |
| format | Article |
| id | doaj.art-207489e1eb0c4ddd948da1d5d37f342a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:43:42Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-207489e1eb0c4ddd948da1d5d37f342a2023-12-03T13:17:30ZengMDPI AGApplied Sciences2076-34172022-08-011216814010.3390/app12168140Complete Blood Cell Detection and Counting Based on Deep Neural NetworksShin-Jye Lee0Pei-Yun Chen1Jeng-Wei Lin2Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, TaiwanInstitute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, TaiwanDepartment of Information Management, Tunghai University, Taichung 407224, TaiwanComplete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.https://www.mdpi.com/2076-3417/12/16/8140blood cell detectionblood cell countingdeep learningconvolutional neural network |
| spellingShingle | Shin-Jye Lee Pei-Yun Chen Jeng-Wei Lin Complete Blood Cell Detection and Counting Based on Deep Neural Networks Applied Sciences blood cell detection blood cell counting deep learning convolutional neural network |
| title | Complete Blood Cell Detection and Counting Based on Deep Neural Networks |
| title_full | Complete Blood Cell Detection and Counting Based on Deep Neural Networks |
| title_fullStr | Complete Blood Cell Detection and Counting Based on Deep Neural Networks |
| title_full_unstemmed | Complete Blood Cell Detection and Counting Based on Deep Neural Networks |
| title_short | Complete Blood Cell Detection and Counting Based on Deep Neural Networks |
| title_sort | complete blood cell detection and counting based on deep neural networks |
| topic | blood cell detection blood cell counting deep learning convolutional neural network |
| url | https://www.mdpi.com/2076-3417/12/16/8140 |
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