Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Abstract = 70 times | PDF = 47 times

Main Article Content

Shakhawan Hares Wady


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.


Leukaemia diagnosis; blood smear; feature extraction; machine learning


Download data is not yet available.

Article Details


[1] C. Di Ruberto, A. Loddo, and G. Puglisi, “Blob detection and deep learning for leukemic blood image analysis,” Appl. Sci., vol. 10, no. 3, 2020, doi: 10.3390/app10031176.
[2] G. Drałus, D. Mazur, and A. Czmil, “Automatic detection and counting of blood cells in smear images using retinanet,” Entropy, vol. 23, no. 11, 2021, doi: 10.3390/e23111522.
[3] B. George-Gay and K. Parker, “Understanding the complete blood count with differential,” J. Perianesthesia Nurs., vol. 18, no. 2, pp. 96–117, 2003, doi: 10.1053/jpan.2003.50013.
[4] G. Soni and K. S. Yadav, “Applications of nanoparticles in treatment and diagnosis of leukemia Applications of nanoparticles in treatment and diagnosis of leukemia,” Mater. Sci. Eng. C, vol. 47, no. April, pp. 156–164, 2018, doi: 10.1016/j.msec.2014.10.043.
[5] S. Shafique and S. Tehsin, “Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks,” Technol. Cancer Res. Treat., vol. 17, pp. 1–7, 2018, doi: 10.1177/1533033818802789.
[6] D. A. Arber et al., “WHO Classification 2016 - Myeloid neoplasms and acute leukemia,” Blood, vol. 127, no. 20, pp. 2391–2405, 2016, doi: 10.1182/blood-2016-03-643544.The.
[7] F. Huang, P. Guang, F. Li, X. Liu, W. Zhang, and W. Huang, “AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research,” Medicine (Baltimore)., vol. 99, no. 45, p. e23154, 2020, doi: 10.1097/MD.0000000000023154.
[8] Y. Dong et al., “Leukemia incidence trends at the global, regional, and national level between 1990 and 2017,” Exp. Hematol. Oncol., vol. 9, no. 1, pp. 1–11, 2020, doi: 10.1186/s40164-020-00170-6.
[9] M. Ghaderzadeh, F. Asadi, A. Hosseini, D. Bashash, H. Abolghasemi, and A. Roshanpour, “Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review,” Sci. Program., vol. 2021, 2021, doi: 10.1155/2021/9933481.
[10] M. Kim, K. Chae, S. Lee, H. J. Jang, and S. Kim, “Automated classification of online sources for infectious disease occurrences using machine-learning-based natural language processing approaches,” Int. J. Environ. Res. Public Health, vol. 17, no. 24, pp. 1–13, 2020, doi: 10.3390/ijerph17249467.
[11] F. E. Al-Tahhan, M. E. Fares, A. A. Sakr, and D. A. Aladle, “Accurate automatic detection of acute lymphatic leukemia using a refined simple classification,” Microsc. Res. Tech., vol. 83, no. 10, pp. 1178–1189, 2020, doi: 10.1002/jemt.23509.
[12] S. H. Wady, “Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors,” UHD J. Sci. Technol., vol. 6, no. 1, pp. 21–33, Feb. 2022, doi: 10.21928/UHDJST.V6N1Y2022.PP21-33.
[13] Z. F. Mohammed and A. A. Abdulla, “An efficient CAD system for ALL cell identification from microscopic blood images,” Multimed. Tools Appl., vol. 80, no. 4, pp. 6355–6368, Oct. 2020, doi: 10.1007/S11042-020-10066-6.
[14] M. Sharif et al., “Recognition of different types of leukocytes using YOLoV2 and optimized bag-of-features,” IEEE Access, vol. 8, pp. 167448–167459, 2020, doi: 10.1109/ACCESS.2020.3021660.
[15] C. Mondal et al., “Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images,” Informatics Med. Unlocked, vol. 27, p. 100794, Jan. 2021, doi: 10.1016/J.IMU.2021.100794.
[16] A. Rehman, N. Abbas, T. Saba, S. I. ur Rahman, Z. Mehmood, and H. Kolivand, “Classification of acute lymphoblastic leukemia using deep learning,” Microsc. Res. Tech., vol. 81, no. 11, pp. 1310–1317, Nov. 2018, doi: 10.1002/JEMT.23139.
[17] S. Kumar, S. Mishra, P. Asthana, and Pragya, “Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm,” Adv. Intell. Syst. Comput., vol. 554, pp. 655–670, 2018, doi: 10.1007/978-981-10-3773-3_64.
[18] A. Setiawan, A. Harjoko, T. Ratnaningsih, E. Suryani, Wiharto, and S. Palgunadi, “Classification of cell types in Acute Myeloid Leukemia (AML) of M4, M5 and M7 subtypes with support vector machine classifier,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-January, pp. 45–49, Apr. 2018, doi: 10.1109/ICOIACT.2018.8350822.
[19] K. Muthumayil, S. Manikandan, S. Srinivasan, J. Escorcia-Gutierrez, M. Gamarra, and R. F. Mansour, “Diagnosis of leukemia disease based on enhanced virtual neural network,” Comput. Mater. Contin., vol. 69, no. 2, pp. 2031–2044, 2021, doi: 10.32604/cmc.2021.017116.
[20] S. Saleem, J. Amin, M. Sharif, M. A. Anjum, M. Iqbal, and S.-H. Wang, “A deep network designed for segmentation and classification of leukemia using fusion of the transfer learning models,” Complex Intell. Syst. 2021, pp. 1–16, Jul. 2021, doi: 10.1007/S40747-021-00473-Z.
[21] V. Singhal and P. Singh, “Texture Features for the Detection of Acute Lymphoblastic Leukemia,” 2016, pp. 535–543.
[22] K. N. Sukhia, M. M. Riaz, A. Ghafoor, and N. Iltaf, “Overlapping white blood cells detection based on watershed transform and circle fitting,” Radioengineering, vol. 26, no. 4, pp. 1177–1181, 2017, doi: 10.13164/re.2017.1177.
[23] 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.
[24] 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.
[25] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002, doi: 10.1109/TPAMI.2002.1017623.
[26] S. H. Wady and H. O. Ahmed, “Ethnicity Identification based on Fusion Strategy of Local and Global Features Extraction,” Int. J. Multidiscip. Curr. Res., vol. 4, no. April, pp. 200–205, 2016.
[27] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognit., vol. 42, no. 3, pp. 425–436, 2009, doi: 10.1016/j.patcog.2008.08.014.
[28] R. Hatibaruah, V. K. Nath, and D. Hazarika, “An effective texture descriptor for retrieval of biomedical and face images based on co-occurrence of similar center-symmetric local binary edges,” Int. J. Comput. Appl., vol. 43, no. 6, pp. 589–600, 2021, doi: 10.1080/1206212X.2019.1590953.
[29] S. Lahmiri and M. Boukadoum, “Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction,” 2011 IEEE 9th Int. New Circuits Syst. Conf. NEWCAS 2011, vol. 2013, pp. 53–56, 2011, doi: 10.1109/NEWCAS.2011.5981217.
[30] 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.
[31] D. Umamaheswari and S. Geetha, “A framework for efficient recognition and classification of Acute Lymphoblastic Leukemia with a Novel Customized-KNN classifier,” J. Comput. Inf. Technol., vol. 26, no. 2, pp. 131–140, 2018, doi: 10.20532/cit.2018.1004123.
[32] M. Tuba and E. Tuba, “Generative adversarial optimization (GOA) for acute lymphocytic leukemia detection,” Stud. Informatics Control, vol. 28, no. 3, pp. 245–254, 2019, doi: 10.24846/v28i3y201901.
[33] 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.