Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning
Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood...
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| Format: | Article |
| Language: | English |
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Sulaimani Polytechnic University
2022-06-01
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| Series: | Kurdistan Journal of Applied Research |
| Subjects: | |
| Online Access: | https://www.kjar.spu.edu.iq/index.php/kjar/article/view/741 |
| _version_ | 1797193721138118656 |
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| author | Shakhawan Hares Wady |
| author_facet | Shakhawan Hares Wady |
| author_sort | Shakhawan Hares Wady |
| collection | DOAJ |
| description | Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier.
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| first_indexed | 2024-03-07T15:26:53Z |
| format | Article |
| id | doaj.art-87a2d2b6c04048fc89bcec8e74f41e51 |
| institution | Directory Open Access Journal |
| issn | 2411-7684 2411-7706 |
| language | English |
| last_indexed | 2024-04-24T05:44:53Z |
| publishDate | 2022-06-01 |
| publisher | Sulaimani Polytechnic University |
| record_format | Article |
| series | Kurdistan Journal of Applied Research |
| spelling | doaj.art-87a2d2b6c04048fc89bcec8e74f41e512024-04-23T17:20:24ZengSulaimani Polytechnic UniversityKurdistan Journal of Applied Research2411-76842411-77062022-06-0110.24017/Science.2022.1.8741Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine LearningShakhawan Hares Wady0Charmo UniversityIdentification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)). As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier. https://www.kjar.spu.edu.iq/index.php/kjar/article/view/741Leukaemia diagnosis; blood smear; feature extraction; machine learning |
| spellingShingle | Shakhawan Hares Wady Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning Kurdistan Journal of Applied Research Leukaemia diagnosis; blood smear; feature extraction; machine learning |
| title | Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning |
| title_full | Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning |
| title_fullStr | Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning |
| title_full_unstemmed | Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning |
| title_short | Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning |
| title_sort | computer aided diagnostic system for blood cells in smear images using texture features and supervised machine learning |
| topic | Leukaemia diagnosis; blood smear; feature extraction; machine learning |
| url | https://www.kjar.spu.edu.iq/index.php/kjar/article/view/741 |
| work_keys_str_mv | AT shakhawanhareswady computeraideddiagnosticsystemforbloodcellsinsmearimagesusingtexturefeaturesandsupervisedmachinelearning |