Red Blood Cell Classification Using Image Processing and CNN

被引：0

作者：

Parab M.A. ^{[2
]}

Mehendale N.D. ^{[1
]}

机构：

[1] K. J. Somaiya College of Engineering, Vidyavihar, Maharashtra, Mumbai

[2] Ninad’s Research Lab, M. G. Road, Maharashtra, Thane

来源：

SN Computer Science | 2021年 / 2卷 / 2期

关键词：

Classification; Convolution neural network; Red blood cells;

D O I：

10.1007/s42979-021-00458-2

中图分类号：

学科分类号：

摘要：

In the medical field, the analysis of the blood sample of the patient is a critical task. Abnormalities in blood cells are accountable for various health issues. Red blood cells (RBCs) are one of the major components of blood. Classifying the RBC can allow us to diagnose different diseases. The traditional, time-consuming technique of visualizing RBC manually under the microscope, is a tedious task and may lead to wrong interpretation because of the human error. The various health conditions can change the shape, texture, and size of normal RBCs. The proposed method has involved the use of image processing to classify the RBCs with the help of convolution neural networks. The algorithm can extract the feature of each segmented cell image and classify it into 9 various types. Images of blood slides were collected from the hospital. The overall accuracy was 98.5%. The system has been developed to provide accurate and fast results that can save patients’ lives. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

引用

共 17 条

[1]

George J.N., Woolf S.H., Raskob G.E., Wasser J., Aledort L., Ballem P., Blanchette V., Bussel J., Cines D., Kelton J., Et al., Idiopathic thrombocytopenic purpura: a practice guideline developed by explicit methods for the American Society of Hematology, Blood., 88, 1, (1996)

[2]

Walker H.K., Hall W.D., Hurst J.W., Peripheral blood smear-clinical methods: the history, physical, and laboratory examinations, (1990)

[3]

Pa T., Basic principles of the ‘Filterability test’(FT) and analysis of erythrocyte flow behavior, Red Cell Rheology, pp. 55-70, (1978)

[4]

Di Ruberto C., Dempster A., Khan S., Jarra B., Analysis of infected blood cell images using morphological operators, Image Vis Comput, 20, 2, (2002)

[5]

Cai R., Qingxiang W., Rui Z., Lijuan F., Chengmei R., Red blood cell segmentation using Active Appearance Model, 2012 IEEE 11Th International Conference on Signal Processing, 3, pp. 1641-1644, (2012)

[6]

Ghosh M., Das D., Chakraborty C., Ray A.K., Automated leukocyte recognition using fuzzy divergence, Micron, 41, 7, (2010)

[7]

Sinha N., Ramakrishnan A.G., Automation of differential blood count, TENCON 2003 Conference on Convergent Technologies for Asia-Pacific Region, 2, pp. 547-551, (2003)

[8]

Piuri V., Fabio S., Morphological classification of blood leucocytes by microscope images, 2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2004. CIMSA, pp. 103-108, (2004)

[9]

Tomari R., Zakaria W.N.W., Jamil M.M.A., Nor F.M., Fuad N.F.N., Computer aided system for red blood cell classification in blood smear image, Procedia Comput Sci, 42, (2014)

[10]

Justus D., John B., Stephen B., Andrew S.M., Predicting the computational cost of deep learning models, 2018 IEEE International Conference on Big Data (Big Data), pp. 3873-3882, (2018)

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