10.14489/vkit.2022.12.pp.046-052

DOI: 10.14489/vkit.2022.12.pp.046-052

Щетинин Е. Ю.
КЛАССИФИКАЦИЯ ЦИТОЛОГИЧЕСКИХ ИЗОБРАЖЕНИЙ ЛЕЙКОЦИТОВ МЕТОДАМИ ГЛУБОКОГО ОБУЧЕНИЯ
(с. 46-52)

Аннотация. Реализован автоматизированный подход к классификации и обнаружению белых кровяных тел на цитологических изображениях клеток крови с использованием методов глубокого обучения. Предложена модель классификации изображений крови LeucoCyteNetv1, которая систематизирует лейкоциты с точностью 98,84 %. Это говорит о возможности ее использования в качестве вспомогательного инструмента для гематологического анализа крови. В архитектуре модели применены разделяемые по глубине сверточные слои SeparableConv2D.

Ключевые слова: лейкоциты; классификация; глубокое обучение; разделяемые сверточные нейронные сети.

Shchetinin E. Yu.
CLASSIFICATION OF CYTOLOGICAL IMAGES OF WHITE BLOOD CELLS USING DEEP LEARNING
(pp. 46-52)

Abstract. Leukocytes, also known as white blood cells, are an important part of the immune system and represent a group of cells that protect the body from infections. The classification of leukocytes is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma, anemia and others. However, traditional methods of classification of leukocytes require a lot of time, and their results are prone to errors. The article implements a computer approach to classification and detection of white blood cells on images of blood cells using deep learning methods based on the application of the method of transferring deep learning models and finetuning them. Deep learning has become one of the most popular areas of artificial intelligence. There are many neural network architectures used in deep learning in solving computer vision problems. The purpose of this study is to develop a computer system for efficient automatic detection and classification of leukocytes of four types of blood cells: eosinophils, lymphocytes, monocytes, neutrophils. This article used popular models of accurate neural networks VGG16, ResNet50, DensNet201, MobileNetV3, InceptionResnetV2, pretrained on ImageNet and finetuned on WBC images dataset.The paper also proposes a custom model of a deep neural network using separable convolutional blocks SeparableConv2D in its architecture. The model is optimized using methods of preprocessing normalization and data augmentation. The is model gave classification metrics of accuracy = 98.84 %, precision = 99.56 %, recall = 98.89 % and f1-score = 99.22 %. The developed model allows, in most cases, to determine with high speed whether a leukocyte belongs to one of the four classes in the image, which indicates the possibility of using the system as an auxiliary tool for hematological blood analysis.

Keywords: Leukocytes; Classification; Deep learning; Separable convolutional neural networks.

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Е. Ю. Щетинин (Департамент математики Финансового университета при Правительстве Российской Федерации, Москва, Россия) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

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E. Yu. Shchetinin (Mathematics Department of the Financial University under the Government of the Russian Federation, Moscow, Russia) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

+ - Библиографический список (References) Click to collapse

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Eng

1. Tkachuk D. C., Hirschmann J. V. (2017). Wintrobe’s Atlas of Clinical Hematology. Lippincott Raven.
2. Volkova S. A., Borovkov N. N. (2013). Fundamentals of clinical hematology: textbook. Nizhniy Novgorod: Izdatel'stvo Nizhegorodskoy gosudarstvennoy meditsinskoy akademii. [in Russian language]
3. Sharma S., Gupta S., Gupta D. et al. (2022). Deep Learning Model for the Automatic Classification of White Blood Cells. Computational Intelligence and Neuroscience, pp. 1 – 13. Article ID 7384131. DOI: 10.1155/2022/7384131
4. Wang Q., Bi S., Sun M. et al. (2019). Deep Learning Approach to Peripheral Leukocyte Recognition. PLoS One, Vol. 14, (6). DOI: 10.1371/journal.pone.0218808
5. Rehman A., Abbas A., Saba N. et al. (2018). Classification of Acute Lymphoblastic Leukemia Using Deep Learning. Microscopy Research and Technique, Vol. 81, (11), pp. 1310 – 1317. DOI: 10.1002/jemt.23139
6. Patil A. M., Patil M. D., Birajdar G. K. (2020). White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis. Innovation and Research in BioMedical Engineering, Vol. 42, (5). DOI: 10.1016/j.irbm.2020.08.005
7. Ozyurt F. (2020). A Fused CNN Model for WBC Detection with Feature Selection and Extreme Learning Machine. Soft Computing, Vol. 24, (11), pp. 8163 – 8172. DOI: 10.1007/s00500-019-04383-8
8. Acevedo A., Alfґerez S., Merino A., Rodellar J. (2019). Recognition of Peripheral Blood Cell Images Using Convolutional Neural Networks. Computer Methods and Programs in Biomedicine, Vol. 180. Article ID 105020. PMID: 31425939. DOI: 10.1016/j.cmpb.2019.105020
9. Yao X., Sun K., Bu X. et al. (2021). Classification of White Blood Cells Using Weighted Optimized Deformable Convolutional Neural Networks. Artificial Cells, Nanomedicine and Biotechnology, Vol. 49, (1), pp. 147 – 155. DOI: 10.1080/21691401.2021.1879823
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11. Chollet F. (2018). Deep Learning with Python. New York: Manning Publishing.
12. Krizhevsky A., Sutskever I., Hinton G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, Vol. 60, (6), pp. 84 – 90. DOI: 10.1145/3065386

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