Diana BABY, Jude HEMANTH, Anishin Raj MM, Sujitha Juliet DEVARAJ

Leukocyte classification based on feature selection using extra trees classifier: a transfer learning approach

The criticality of investigating the white blood cell (WBC) count cannot be underestimated, as white blood cells are an important component of the body’s defence system. From helping to diagnose hidden infections to insinuating the presence of comorbidities like immunodeficiency, an accurate white blood cell count can contribute significantly to shape a physician’s assessment. The manual process performed by the pathologists for the classification of WBCs is a time consuming and tedious task, which is further disadvantaged by a lack of accuracy. This study concentrates on the automatic detection and classification of WBC without data augmentation into four subtypes such as eosinophil, monocyte, lymphocyte and neutrophil based on images from three different datasets. The methodology adopted in this paper is transfer learning approach in which the features are extracted using ResNet50, DenseNet121, MobileNetv2, Inceptionv3 and Xception deep learning models.The extra trees classifier is used as an intermediate stage for selecting most predominant features, which reduce the execution time. When evaluating the performance on the basis of recall, precision, F-measure and accuracy parameters, the classification of ResNet50 features selected by extra trees classifier using multi-class support vector machine (SVM) provides the highest accuracy of 90.76% .

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[1] Adewoyin AS, Nwogoh B. Peripheral blood film - a review. Annals of Ibadan postgraduate medicine 2014; 12 (2): 71-79. doi: 10.4314/AIPM.V12I2
[2] Basu D, Kulkarni R. Overview of blood components and their preparation. Indian Journal of Anaesthesia 2014; 58 (5): 529-537. doi: 10.4103/0019-5049.144647
[3] Young IT. The classification of white blood cells. IEEE Transactions on Biomedical Engineering 1972; 19 (4): 291-298. doi: 10.1109/TBME.1972.324072
[4] Prinyakupt J, Pluempitiwiriyawej C. Segmentation of white blood cells and comparison of cell morphology by linear and naive Bayes classifiers. Biomedical engineering online 2015; 14 (63): 1-19. doi: 10.1186/s12938-015-0037-1
[5] Patel S, Patel A. Deep Leaning Architectures and its Applications A Survey. International Journal of Computer Sciences and Engineering 2018; 6 (6): 1177-1183. doi: 10.26438/ijcse/v6i6.11771183
[6] Mirčić S, Jorgovanovic N. Automatic classification of leukocytes. Journal of Automatic Control 2006; 16 (1): 29-32. doi: 10.2298/JAC0601029M
[7] Çınar A, Tuncer SA. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Applied Sciences 2021; 3 (4): 1-11. doi: 10.1007/s42452-021-04485-9
[8] Tiwari P, Qian J, Li Q, Wang B, Gupta D et al. Detection of Subtype Blood Cells using Deep Learning. Cognitive Systems Research 2018; 52: 1036-1044. doi: 10.1016/j.cogsys.2018.08.022
[9] AL-Dulaimi K,Banks J,Chandran V,Tomeo-Reyes I, Nguyen K. Classification of White Blood Cell Types from Microscope Images: Techniques and Challenges. In: Mendez-Vilas A, Torres-Hergueta E (editors). Microscopy science: Last approaches on educational programs and applied research. Microscopy Book Series, 8, Spain: Formatex Research Center, 2018, pp. 17-25.
[10] Sarrafzadeh O, Rabbani H, Talebi H, Banaem HU. Selection of the best features for leukocytes classification in blood smear microscopic images. In: SPIE Medical Imaging 2014: Digital Pathology conference; San Diego, California, United States; 2014. pp. 1-9.
[11] Reena MR, Ameer PM. Localization and recognition of leukocytes in peripheral blood: A deep learning approach. Computers in Biology and Medicine 2020; 126: 1-12. doi: 10.1016/j.compbiomed.2020.104034
[12] Umpon NT, Dhompongsa S. Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Transactions on Information Technology in Biomedicine 2007; 11 (3): 353–359. doi: 10.1109/titb.2007.892694
[13] Huang X, Liu J, Yao J, Wei M, Han W et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021; 21 (2): 1-14. doi: 10.3390/s21020512
[14] Maier A, Syben C, Lasser T, Riess C. A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik 2019; 29 (2): 86-101. doi: 29. 10.1016/j.zemedi.2018.12.003
[15] Mohamed EH, El-Behaidy WH, Khoriba G, Li J. Improved White Blood Cells Classification based on Pretrained Deep Learning Models. Journal of Communications Software and Systems 2020; 16 (1): 1-9. doi: 10.24138/jcomss.v16i1.818
[16] Su M, Cheng C, Wang P. A Neural-Network-Based Approach to White Blood Cell Classification. The Scientific World Journal 2014, 2014: 1-9. doi: 10.1155/2014/796371
[17] Alam MM, Islam MT. Machine learning approach of automatic identification and counting of blood cells. Healthcare technology letters 2019; 6 (4): 103-108. doi: 10.1049/htl.2018.5098
[18] Wang Q, Bi S, Sun M, Wang Y, Wang D et al. Deep learning approach to peripheral leukocyte recognition. PLoS ONE 2019; 14 (6): 1-9. doi: 10.1371/journal.pone.0218808
[19] Huang G, Liu Z, Maaten LVD, Weinberger KQ. Densely Connected Convolutional Networks. In: IEEE 2017 Computer Vision and Pattern Recognition Conference; Honolulu, HI, USA; 2017. pp. 2261-2269.
[20] Zhuang F, Qi Z, Duan K, Xi D, Zhu Y et al.A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE 2020; 109 (1): 43-76. doi: 10.1109/JPROC.2020.3004555
[21] Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. Journal of Big Data 2016; 3 (9): 1-40. doi: 10.1186/s40537-016-0043-6
[22] Yildirim M, Çinar A. Classification of white blood cells by deep learning methods for diagnosing disease. Revue d’Intelligence Artificielle 2019; 33 (5): 335-340. doi: 10.18280/ria.330502
[23] Almezhghwi K, Serte S. Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network.Computational Intelligence and Neuroscience 2020; 2020: 1-12. doi: 10.1155/2020/6490479
[24] Kutlu H, Avci E, Özyurt F. White Blood Cells Detection and Classification Based on Regional Convolutional Neural Networks. Medical Hypotheses 2019; 135 (10): 1-17. doi: 10.1016/j.mehy.2019.109472
[25] Vatathanavaro S, Tungjitnob S, Pasupa K. White Blood Cell Classification: A Comparison between VGG-16 and ResNet-50 Models. In: IEEE 2018 6th Joint Symposium on Computational Intelligence Conference; Bangkok, Thailand; 2018. pp. 1-2.
[26] Rajaraman S, Antani SK,Poostchi M,Silamut K, Hossain MA. Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ 2018; 6 (1): 1-17. doi: 10.7717/peerj.4568
[27] He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: IEEE 2016 Computer Vision and Pattern Recognition Conference; Las Vegas, NV, USA; 2016. pp. 770-778.
[28] Khan RU, Zhang X, Kumar R, Aboagye EO. Evaluating the Performance of ResNet Model Based on Image Recognition. In: 2018 Computing and Artificial Intelligence International Conference; Chengdu, China; 2018. pp. 86-90.
[29] Peng J, Kang S, Ning Z,Deng H, Shen J et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. European Radiology 2019; 30 (1): 413-424. doi: 10.1007/s00330-019-06318-1
[30] Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: IEEE/CVF 2018 Computer Vision and Pattern Recognition Conference; Salt Lake City, UT, USA; 2018. pp. 4510-4520.
[31] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In : IEEE 2016 Computer Vision and Pattern Recognition Conference; Las Vegas, NV, USA ; 2016. pp. 2818-2826.
[32] Wang C, Chen D, Hao L, Liu X, Zeng Y et al. Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model. IEEE Access 2019; 7: 146533-146541. doi: 10.1109/ACCESS.2019.2946000
[33] Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. IEEE 2017 on Computer Vision and Pattern Recognition Conference; Honolulu, HI, USA; 2017. pp. 1800-1807.
[34] Sharaff A, Gupta H. Extra-Tree Classifier with Metaheuristics Approach for Email Classification. In: Bhatia S, Tiwari S, Mishra K, Trivedi M (editors). Advances in Computer Communication and Computational Sciences: Advances in Intelligent Systems and Computing, Singapore: Springer, 2019, pp. 189-197.
[35] Osowski S, Siroic R, Markiewicz T, Siwek K. Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Transactions on Instrumentation and Measurement 2009; 58 (7): 2159–2168. doi: 10.1109/TIM.2008.2006726
[36] Chen H, Haoyu C. Face Recognition Algorithm Based on VGG Network Model and SVM. In: 2019 3rd Machine Vision and Information Technology International Conference; Guangzhou, China; 2019. pp.1-8.
[37] Thai LH, Hai TS, Thuy NT. Image Classification using Support Vector Machine and Artificial Neural Network. International Journal of Information Technology and Computer Science 2012; 5: 32-38. doi: 10.5815/ijitcs.2012.05.05
[38] Zheng X, Wang Y,Wang G, Liu J. Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 2018; 107: 55-71. doi: 10.1016/j.micron.2018.01.010
[39] Powers DMW. Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation. Journal of Machine Learning Technologies 2011; 2 (1): 37-63. doi: 10.9735/2229-3981
[40] Sharma M, Bhave A, Janghel RR. White Blood Cell Classification Using Convolutional Neural Network. In: Wang J, Reddy G, Prasad V, Reddy V (editors). Soft Computing and Signal Processing: Advances in Intelligent Systems and Computing, Singapore: Springer, 2019, pp. 135-143.
[41] Jiang M, Cheng L, Qin F, Du L, Zhang M. White Blood Cells Classification with Deep Convolutional Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence 2018; 32 (9): 1-11. doi: 10.1142/S0218001418570069