Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review
Blood cell detection considers a gold standard key in diagnosing blood disease and producing automatic reports to hematologists and doctors. Blood cell detection is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platel...
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| Format: | Article |
| Language: | English |
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College of Education, Al-Iraqia University
2023-02-01
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| Series: | Iraqi Journal for Computer Science and Mathematics |
| Subjects: | |
| Online Access: | https://journal.esj.edu.iq/index.php/IJCM/article/view/470 |
| _version_ | 1827781480656404480 |
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| author | KHAMAEL AL-DULAIMI Teba Makki |
| author_facet | KHAMAEL AL-DULAIMI Teba Makki |
| author_sort | KHAMAEL AL-DULAIMI |
| collection | DOAJ |
| description |
Blood cell detection considers a gold standard key in diagnosing blood disease and producing automatic reports to hematologists and doctors. Blood cell detection is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. Traditional procedure of blood cell detection requires pathologist effort and time. In computer aided diagnosis, machine learning and deep learning techniques become the practical way to automate the procedure of diagnosing, classify microscopic blood cells, and increase the accuracy and speed of the procedure. This paper provides a review of the detection and classification of blood cell, including red blood cells, white blood cells and platelets and their characteristics using machine learning techniques. We also have detailed the dataset of microscope blood cell. We have divided the previous works into four categories based on the output of the models, including pre-processing, segmentation, feature extraction and classification. Then, we discuss the challenges that face these methods and suggest the potential future techniques.
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| first_indexed | 2024-03-11T15:19:18Z |
| format | Article |
| id | doaj.art-9109a950b8404b7b983e9f314921616f |
| institution | Directory Open Access Journal |
| issn | 2958-0544 2788-7421 |
| language | English |
| last_indexed | 2024-03-11T15:19:18Z |
| publishDate | 2023-02-01 |
| publisher | College of Education, Al-Iraqia University |
| record_format | Article |
| series | Iraqi Journal for Computer Science and Mathematics |
| spelling | doaj.art-9109a950b8404b7b983e9f314921616f2023-10-29T06:16:20ZengCollege of Education, Al-Iraqia UniversityIraqi Journal for Computer Science and Mathematics2958-05442788-74212023-02-014210.52866/ijcsm.2023.02.02.002Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A ReviewKHAMAEL AL-DULAIMI0Teba Makki1Queensland University of TechnologyAl-Nahrain University, Computer Science, Baghdad, Iraq Blood cell detection considers a gold standard key in diagnosing blood disease and producing automatic reports to hematologists and doctors. Blood cell detection is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. Traditional procedure of blood cell detection requires pathologist effort and time. In computer aided diagnosis, machine learning and deep learning techniques become the practical way to automate the procedure of diagnosing, classify microscopic blood cells, and increase the accuracy and speed of the procedure. This paper provides a review of the detection and classification of blood cell, including red blood cells, white blood cells and platelets and their characteristics using machine learning techniques. We also have detailed the dataset of microscope blood cell. We have divided the previous works into four categories based on the output of the models, including pre-processing, segmentation, feature extraction and classification. Then, we discuss the challenges that face these methods and suggest the potential future techniques. https://journal.esj.edu.iq/index.php/IJCM/article/view/470Blood cellDetectionReviewComputer Aided DiagnosisMachine Learning |
| spellingShingle | KHAMAEL AL-DULAIMI Teba Makki Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review Iraqi Journal for Computer Science and Mathematics Blood cell Detection Review Computer Aided Diagnosis Machine Learning |
| title | Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review |
| title_full | Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review |
| title_fullStr | Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review |
| title_full_unstemmed | Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review |
| title_short | Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review |
| title_sort | blood cell microscopic image classification in computer aided diagnosis using machine learning a review |
| topic | Blood cell Detection Review Computer Aided Diagnosis Machine Learning |
| url | https://journal.esj.edu.iq/index.php/IJCM/article/view/470 |
| work_keys_str_mv | AT khamaelaldulaimi bloodcellmicroscopicimageclassificationincomputeraideddiagnosisusingmachinelearningareview AT tebamakki bloodcellmicroscopicimageclassificationincomputeraideddiagnosisusingmachinelearningareview |