LÖKOSİT TESPİTİ İÇİN BEYAZ KAN HÜCRELERİNİN ESA KULLANILARAK SINIFLANDIRILMASI

Year 2022, Volume: 9 Issue: 17, 333 - 344, 31.08.2022

Mucella Özbay Karakuş , Erdal Özbay

https://doi.org/10.54365/adyumbd.1069856

Cited By: 1

Abstract

Beyaz kan hücreleri, insanların bağışıklık sisteminin en önemli yapısı olup, kan ve lenf dokularında kemik iliği tarafından üretilmektedir. Bu hücreler insan vücudunu hastalık ve yabancı organizmalara karşı koruyan savunma mekanizmalarıdır ve kandaki oranı düştüğünde Lökopeni ile karşılaşılabilir. Bu hücrelerin insan vücudundaki oranının belirlenmesi ve hastalığın tespit ve tedavisi için yoğun emek harcaması gerekmektedir. Bu çalışmada, derin öğrenme modellerini kullanarak beyaz kan hücreleri sınıflandırma performansının iyileştirilmesi amaçlanmıştır. Sınıflandırma işlemini daha verimli gerçekleştirmek için evrişimli sinir ağı modelleri kullanılmıştır. Beyaz kan hücresi çeşitleri olan eozinofil, lenfosit, monosit ve nötrofil arasında ayrım yapmak için Densenet201, ResNet50 ve Alexnet birleştirilmiştir. Elde edilen özellik haritalarının sınıflandırılması için K-En yakın komşuluk, Destek Vektör Makinesi ve Naïve Bayes olmak üzere üç farklı makine öğrenmesi sınıflandırıcısı kullanılmıştır. Derin Öğrenme (DÖ) ile eğitilen Kaggle veri kümesi görüntülerine CLAHE ve Gauss filtreleri uygulanarak bu görüntüler üç ESA ağı ile yeniden sınıflandırılmıştır. Bu filtreler uygulandıktan sonra elde edilen sonuçların, orijinal verilerle elde edilen sınıflandırma sonuçlardan daha yüksek olduğu ortaya konmuştur.

Keywords

Beyaz kan hücreleri, Derin öğrenme, ESA, Görüntü işleme, Sınıflandırma

CLASSIFICATION OF WHITE BLOOD CELLS USING CNN FOR THE DETECTION OF LEUCOCYTE

Abstract

White blood cells are the most important structure of the human immune system and are produced by the bone marrow in the blood and lymph tissues. These cells are the defense mechanisms that protect the human body against diseases and foreign organisms, and Leukopenia may be encountered when the rate in the blood decreases. Intensive effort is required to determine the ratio of these cells in the human body and to detect and treat the disease. In this study, it is aimed to improve the white blood cell classification performance by using deep learning models. Convolutional neural network models are used to perform the classification process more efficiently. Densenet201, ResNet50, and Alexnet were combined to distinguish between the white blood cell variants, eosinophils, lymphocytes, monocytes, and neutrophils. Three different machine learning classifiers, namely K-Nearest Neighborhood, Support Vector Machine and Naïve Bayes, were used to classify the obtained feature maps. By applying CLAHE and Gaussian filters to Kaggle dataset images trained with Deep Learning (DL), these images were reclassified with three CNN networks. It has been revealed that the results obtained after applying these filters are higher than the classification results obtained with the original data.

Keywords

White blood cells, Deep learning, CNN, Image processing, Classification

References

• Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. Journal of biophotonics 2019; 12(7): 201800488.
• Janjua HU, Akhtar M, Hussain F. Effects of sugar, salt and distilled water on white blood cells and platelet cells: A review. Journal of Tumor 2016; 4(1): 354-358.
• Wang Q, Bi S, Sun M, Wang Y, Wang D, Yang S. Deep learning approach to peripheral leukocyte recognition. PloS one 2019; 14(6): 0218808.
• Kabat GC, Kim MY, Manson JE, Lessin L, Lin J. Wassertheil-Smoller S, Rohan TE. White blood cell count and total and cause-specific mortality in the Women's Health Initiative. American journal of epidemiology 2017; 186(1): 63-72.
• Fest J, Ruiter R, Ikram MA, Voortman T, van Eijck CH, Stricker BH. Reference values for white blood-cell-based inflammatory markers in the Rotterdam Study: a population-based prospective cohort study. Scientific reports 2018; 8(1): 1-7.
• Torre LA, Islami F, Siegel RL, Ward EM, Jemal A. Global cancer in women: burden and trends. Cancer Epidemiology and Prevention Biomarkers 2017; 26(4): 444-457.
• Weitkamp E, Mermikides A. Medical performance and the ‘inaccessible’experience of illness: an exploratory study. Medical humanities 2016; 42(3): 186-193.
• Sajjad M, Khan S, Jan Z, Muhammad K, Moon H, Kwak JT, Rho S, Baik SW, Mehmood I. Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 2016; 5: 3475-3489.
• Sharma M, Bhave A, Janghel RR. White blood cell classification using convolutional neural network. In Soft Computing and Signal Processing 2019; Springer: 135-143.
• Wang JL, Li AY, Huang M, Ibrahim AK, Zhuang H, Ali AM. Classification of white blood cells with patternnet-fused ensemble of convolutional neural networks (pecnn). IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2018; 325-330.
• Mooney P. Blood Cell Images. 2018 (Erişim: 07.02.2022), https://www.kaggle.com/paultimothymooney/blood-cells
• Rojas I, Valenzuela O, Rojas F, Ortuño F. Bioinformatics and Biomedical Engineering: 7th International Work-Conference. Proceedings 2019; Part I. (11465).
• Ma L, Shuai R, Ran X, Liu W, Ye C. Combining DC-GAN with ResNet for blood cell image classification. Medical & biological engineering & computing 2020; 58(6): 1251-1264.
• Şengür A, Akbulut Y, Budak Ü, Cömert Z. White blood cell classification based on shape and deep features. International Artificial Intelligence and Data Processing Symposium (IDAP) 2019; 1-4.
• Patil AM, Patil MD, Birajdar GK. White blood cells image classification using deep learning with canonical correlation analysis. IRBM 2021; 42(5): 378-389.
• Hotelling H. Relations between two sets of variates. Breakthroughs in statistics 1992; 162-190.
• 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.
• Sharma S, Gupta S, Gupta D, Juneja S, Gupta P, Dhiman G. Kautish S. Deep learning model for the automatic classification of white blood cells. Computational Intelligence and Neuroscience; 2022.
• Girdhar A, Kapur H, Kumar V. Classification of White blood cell using Convolution Neural Network. Biomedical Signal Processing and Control 2022; 71: 103156.
• Yu W, Chang J, Yang C, Zhang L, Shen H, Xia Y, Sha J. Automatic classification of leukocytes using deep neural network. 12th international conference on ASIC (ASICON) 2017: 1041-1044.
• Macawile MJ, Quiñones VV, Ballado A, Cruz JD, Caya MV. White blood cell classification and counting using convolutional neural network. 3rd International conference on control and robotics engineering (ICCRE) 2018: 259-263.
• Zhao J, Zhang M, Zhou Z, Chu J, Cao F. Automatic detection and classification of leukocytes using convolutional neural networks. Medical & biological engineering & computing 2017; 55(8): 1287-1301.
• Ming Y, Zhu E, Wang M, Ye Y, Liu X, Yin J. DMP-ELMs: Data and model parallel extreme learning machines for large-scale learning tasks. Neurocomputing 2018; 320: 85-97.
• Imran Razzak M, Naz S. Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2017: 49-55.
• Hegde RB, Prasad K, Hebbar H, Singh BMK. Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybernetics and Biomedical Engineering 2019; 39(2): 382-392.
• Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug discovery today 2018; 23(6): 1241-1250.
• Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL Zhu J, Li C, Hewett S, Dong J, Ziyar I,Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018; 172(5): 1122-1131.
• Molchanov P, Tyree S, Karras T, Aila T, Kautz J. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv 2016; 1611.06440.
• Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition 2017: 4700-4708.
• He T, Zhang Z, Zhang H, Zhang Z, Xie J, Li M. Bag of tricks for image classification with convolutional neural networks. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019: 558-567.
• Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Advances in neural information processing systems 2017; 60(6): 84-90.
• Shahzad A, Raza M, Shah JH, Sharif M, Nayak RS. Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. Complex & Intelligent Systems 2021: 1-17.
• Izanloo R, Fakoorian SA, Yazdi HS, Simon D. Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise. Annual Conference on Information Science and Systems (CISS) 2016: 500-505.
• Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR. Neighbourhood components analysis. Advances in neural information processing systems 2004; 17.