Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning
https://doi.org/10.24017/Science.2022.1.8
Abstract views: 1023 / PDF downloads: 570Abstract
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.
Keywords:
Leukaemia diagnosis; blood smear; feature extraction; machine learning
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https://doi.org/10.21928/uhdjst.v6n1y2022.pp21-33
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https://doi.org/10.1007/s11042-020-10066-6
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https://doi.org/10.1109/ACCESS.2020.3021660
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https://doi.org/10.1016/j.imu.2021.100794
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https://doi.org/10.1002/jemt.23139
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https://doi.org/10.1007/978-981-10-3773-3_64
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https://doi.org/10.1109/ICOIACT.2018.8350822
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https://doi.org/10.32604/cmc.2021.017116
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https://doi.org/10.1007/s40747-021-00473-z
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https://doi.org/10.1007/978-981-10-0135-2_52
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https://doi.org/10.13164/re.2017.1177
[23] F. H. Ahmad and S. H. Wady, "COVID?19 Infection Detection from Chest X?Ray Images Using Feature Fusion and Machine Learning," Sci. J. Cihan Univ. - Sulaimaniya, vol. 5, no. 2, pp. 10-30, 2021.
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https://doi.org/10.1109/TPAMI.2002.1017623
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https://doi.org/10.1016/j.patcog.2008.08.014
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https://doi.org/10.1080/1206212X.2019.1590953
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https://doi.org/10.1109/NEWCAS.2011.5981217
[29] L. Zhou and H. Wang, "Local gradient increasing pattern for facial expression recognition," in Proceedings - International Conference on Image Processing, ICIP, 2012, pp. 2601-2604, doi: 10.1109/ICIP.2012.6467431.
https://doi.org/10.1109/ICIP.2012.6467431
[30] I. I. Conference and I. Processing, "ALL-IDB?: The Acute Lymphoblastic Leukemia Image Database For Image Processing Ruggero Donida Labati , Vincenzo Piuri , Fabio Scotti Università degli Studi di Milano , Department of Information Technology ," Ieee Int. Conf. Image Process., pp. 2089-2092, 2011.
[31] P. K. Das, P. Jadoun, and S. Meher, "Detection and Classification of Acute Lymphocytic Leukemia," Proc. 2020 IEEE-HYDCON Int. Conf. Eng. 4th Ind. Revolution, HYDCON 2020, Sep. 2020, doi: 10.1109/HYDCON48903.2020.9242745.
https://doi.org/10.1109/HYDCON48903.2020.9242745
[32] S. Praveena and S. P. Singh, "Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia," Biomed. Tech., vol. 65, no. 6, pp. 759-773, 2020, doi: 10.1515/bmt-2018-0213.
https://doi.org/10.1515/bmt-2018-0213
[33] P. K. Das, S. Meher, R. Panda, and A. Abraham, "An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders," IEEE Trans. Cybern., vol. PP, 2021, doi: 10.1109/TCYB.2021.3062152.
https://doi.org/10.1109/TCYB.2021.3062152
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How to Cite
[1]
S. H. Wady, “Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning”, KJAR, vol. 7, no. 1, pp. 86–103, Jun. 2022, doi: 10.24017/Science.2022.1.8.
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19-06-2022
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