Classification of Blood Cell Types Using CNN

Authors

  • D Navya
  • E Krishna
  • Dr. J Rajaram
  • S Raju

Keywords:

Basophils, Eosinophil, Monocytes, Lymphocytes, and Neutrophils are all types of blood cells. A framework for deep learning called TensorFlow In the name of Keras function of softmax The Relufunction's. a kind of leukocyte Google's joint venture

Abstract

White blood cells, commonly referred to as leukocytes, play a critical part in human immunity development and maintenance. Classifying White Blood Cells plays a critical function in diagnosing sickness in a person. Using the classification, disorders including infections, allergies, anaemia, leukaemia, cancer, and the Acquired Immune Deficiency Syndrome (AIDS), which are caused by aberrations in the immune system, may be more accurately identified and treated as a result. To help haematologists identify the kind of White Blood Cells present in the human body and uncover the root cause of disorders, this categorization is necessary. There is now a lot of study being done in this area. A deep learning technology called Convolution Neural Networks (CNN) will be used to classify WBC pictures into four subtypes, namely neutrophil, eosinophil, lymphocyte, and monocyte, since classifying WBCs has enormous potential. In this work, we'll present the results of a number of experiments on the Blood Cell Classification and Detection (BCCD) dataset, which we used to train CNNs.

Downloads

Download data is not yet available.

References

Maton, A.: Human Biology and Health. Prentice Hall, Englewood Cliffs (1993)

LaFleur-Brooks, M.: Exploring Medical Language: A Student-Directed Approach, 7th edn, p. 398. Mosby Elsevier, St. Louis Missouri (2008). ISBN 978-0-323-04950-4

Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Molecular Biology of the Cell, p. 1367. Garland Science, New York (2002) AQ4

Kampbell, N.A.: Biology. Benjamin Cummings, San Francisco (n.d.) AQ5

NCI Dictionary of Cancer Terms (n.d.). https://www.cancer.gov/publications/ dictionaries/cancer- terms/

Macawile, M.J., Quinones, V.V., Ballado, A., Cruz, J.D., Caya, M.V.: White blood cell classification and counting using convolutional neural network. In: 2018 3rd International Conference on Control and Robotics Engineering (ICCRE) (2018) Author Proof 12 I. Singh et al.

Al-Dulaimi, K., Chandran, V., Banks, J., Tomeo-Reyes, I., Nguyen, K.: Classification of white blood cells using bispectral invariant features of nuclei shape. In: 2018 Digital Image Computing: Techniques and Applications (DICTA) (2018)

Dertat, A.: Applied Deep Learning - Part 4: Convolutional Neural Networks, Medium, 13 November 2017. https://towardsdatascience.com/applieddeep-learning-part-4- convolutional-neural-networks-584bc134c1e2

Prabhu: Understanding of Convolutional Neural Network (CNN) - Deep Learning, Medium, 21 November 2019. https://medium.com/@RaghavPrabhu/ understanding-of- convolutional-neural-network-cnn-deep-learning- 99760835f148

Dernoncourt, F.: What is batch size in neural network? Cross Validated, 01 June 1965.

https://stats.stackexchange.com/questions/153531/what-is- batchsizein-neural-network

Daniel: What’s is the difference between train, validation and test set, in neural networks? Stack Overflow, 01 July 1960. https://stackoverflow.com/questions/ 2976452/whats-is-the- difference-between-train-validation-and-test-set-in- neuralnetwo

LNCS Home Page. http://www.springer.com/lncs. Accessed 4 Oct 2017

A Comprehensive Guide to Convolutional Neural Networks

- the ELI5 way. https://towardsdatascience.com/a- comprehensive-guide-to-convolutional-neuralnetworks-the- eli5-way-3bd2b1164a53. Accessed 17 Dec 2018

ReLU. https://www.tinymind.com/learn/terms/relu.

Accessed 30 Oct 2017

Di Ruberto, C., Putzu, L.: Accurate blood cells segmentation through intuitionistic fuzzy set threshold. In: 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, pp. 57–64, November 2014. https://doi. org/10.1109/SITIS.2014.43

Blood Cell Images. https://www.kaggle.com/paultimothymooney/blood-cells. Accessed 21 Apr 2018

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929– 1958 (2014)

Downloads

Published

2020-12-30

How to Cite

Navya, D. . ., Krishna, E. . ., Rajaram, D. J. . ., & Raju, S. . . (2020). Classification of Blood Cell Types Using CNN. The Journal of Contemporary Issues in Business and Government, 26(3), 134–139. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/528

Most read articles by the same author(s)