Research Article
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Year 2022, , 462 - 476, 31.12.2022

Oğuzhan Katar , İlhan Fırat Kılınçer

https://doi.org/10.35377/saucis...1196934
Cited By: 1

Abstract

References

  • [1] C. J. Walsh and C. A. Luer, "Elasmobranch hematology: identification of cell types and practical applications," The Elasmobranch Husbandry Manual: Captive Care of Sharks, Rays and their Relatives, pp. 307-323, 2004.
  • [2] A. Glenn and C. E. Armstrong, "Physiology of red and white blood cells," Anaesthesia & Intensive Care Medicine, vol. 20, no. 3, pp. 180-174, 2019.
  • [3] R. Van Zwieten, A. J. Verhoeven and D. Roos, "Inborn defects in the antioxidant systems of human red blood cells," Free Radical Biology and Medicine, vol. 67, pp. 377-386, 2014.
  • [4] I. Andia and N. Maffulli, "Platelet-rich plasma for managing pain and inflammation in osteoarthritis," Nature Reviews Rheumatology, vol. 9, no. 12, pp. 721-730, 2013.
  • [5] B. Olas and B. Wachowicz, "Role of reactive nitrogen species in blood platelet functions," Platelets, vol. 18, no. 8, pp. 555-565, 2007.
  • [6] M. Habibzadeh, M. Jannesari, Z. Rezaei, H. Baharvand and M. Totonchi, "Automatic white blood cell classification using pre-trained deep learning models: Resnet and inception," Tenth international conference on machine vision, vol. 10696, pp. 274-281, 2018.
  • [7] A. Shahzad, M. Raza, J. H. Shah, M. Sharif and R. S. Nayak, "Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization," Complex & Intelligent Systems, vol. 8, no. 4, pp. 3143-3159, 2022.
  • [8] O. Katar and E. Duman, "Deep Learning Based Covid-19 Detection With A Novel CT Images Dataset: EFSCH-19," Avrupa Bilim ve Teknoloji Dergisi, vol. 29, pp. 150-155, 2021.
  • [9] S. Sharma, S. Gupta, D. Gupta, S. Juneja, P. Gupta, G. Dhiman and S. Kautish, "Deep learning model for the automatic classification of white blood cells," Computational Intelligence and Neuroscience, 2022.
  • [10] M. J. Macawile, V. V. Quiñones, A. Ballado, J. D. Cruz and M. V. Caya, "White blood cell classification and counting using convolutional neural network," 3rd International conference on control and robotics engineering (ICCRE), pp. 259-263, 2018.
  • [11] Y. Wang and Y. Cao, "Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation," Medical physics, vol. 47, no. 1, pp. 142-151, 2020.
  • [12] M. Sharma, A. Bhave and R. R. Janghel, "White blood cell classification using convolutional neural network," Soft Computing and Signal Processing , pp. 135-143, 2019.
  • [13] S. H. Rezatofighi and H. Soltanian-Zadeh, "Automatic recognition of five types of white blood cells in peripheral blood," Computerized Medical Imaging and Graphics, vol. 35, no. 4, pp. 333-343, 2011.
  • [14] M. Mohamed, B. Far and A. Guaily, "An efficient technique for white blood cells nuclei automatic segmentation," 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 220-225, 2012.
  • [15] O. Sarrafzadeh, H. Rabbani, A. Talebi and H. U. Banaem, "Selection of the best features for leukocytes classification in blood smear microscopic images," Medical Imaging 2014: Digital Pathology, vol. 9041, pp. 159-166, 2014.
  • [16] R. D. Labati, V. Piuri and F. Scotti, "All-IDB: The acute lymphoblastic leukemia image database for image processing," 18th IEEE international conference on image processing, pp. 2045-2048, 2011.
  • [17] X. Zheng, Y. Wang, G. Wang and J. Liu, "Fast and robust segmentation of white blood cell images by self-supervised learning," Micron, vol. 107, pp. 55-71, 2018.
  • [18] Z. M. Kouzehkanan et al., "A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm," Scientific reports, vol. 12, no. 1, pp. 1-14, 2022.
  • [19] J. Deng, W. Dong, R. Socher, L. J. Li, K. Li and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," IEEE conference on computer vision and pattern recognition, pp. 248-255, 2009.
  • [20] M. Yildirim and A. Çinar, "Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease," Rev. d'Intelligence Artif., vol. 33, no. 5, pp. 335-340, 2019.
  • [21] C. Jung, M. Abuhamad, J. Alikhanov, A. Mohaisen, K. Han and D. Nyang, "W-net: a CNN-based architecture for white blood cells image classification," arXiv preprint arXiv:1910.01091, 2019.
  • [22] A. Ekiz, K. Kaplan and H. M. Ertunç, "Classification of white blood cells using CNN and Con-SVM," 29th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2021.
  • [23] A. Girdhar, H. Kapur and V. Kumar, "Classification of White blood cell using Convolution Neural Network," Biomedical Signal Processing and Control, vol. 71, 2022.

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

Year 2022, , 462 - 476, 31.12.2022

Oğuzhan Katar , İlhan Fırat Kılınçer

https://doi.org/10.35377/saucis...1196934
Cited By: 1

Abstract

White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.

Keywords

white blood cells classification pre-trained models artificial intelligence

References

  • [1] C. J. Walsh and C. A. Luer, "Elasmobranch hematology: identification of cell types and practical applications," The Elasmobranch Husbandry Manual: Captive Care of Sharks, Rays and their Relatives, pp. 307-323, 2004.
  • [2] A. Glenn and C. E. Armstrong, "Physiology of red and white blood cells," Anaesthesia & Intensive Care Medicine, vol. 20, no. 3, pp. 180-174, 2019.
  • [3] R. Van Zwieten, A. J. Verhoeven and D. Roos, "Inborn defects in the antioxidant systems of human red blood cells," Free Radical Biology and Medicine, vol. 67, pp. 377-386, 2014.
  • [4] I. Andia and N. Maffulli, "Platelet-rich plasma for managing pain and inflammation in osteoarthritis," Nature Reviews Rheumatology, vol. 9, no. 12, pp. 721-730, 2013.
  • [5] B. Olas and B. Wachowicz, "Role of reactive nitrogen species in blood platelet functions," Platelets, vol. 18, no. 8, pp. 555-565, 2007.
  • [6] M. Habibzadeh, M. Jannesari, Z. Rezaei, H. Baharvand and M. Totonchi, "Automatic white blood cell classification using pre-trained deep learning models: Resnet and inception," Tenth international conference on machine vision, vol. 10696, pp. 274-281, 2018.
  • [7] A. Shahzad, M. Raza, J. H. Shah, M. Sharif and R. S. Nayak, "Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization," Complex & Intelligent Systems, vol. 8, no. 4, pp. 3143-3159, 2022.
  • [8] O. Katar and E. Duman, "Deep Learning Based Covid-19 Detection With A Novel CT Images Dataset: EFSCH-19," Avrupa Bilim ve Teknoloji Dergisi, vol. 29, pp. 150-155, 2021.
  • [9] S. Sharma, S. Gupta, D. Gupta, S. Juneja, P. Gupta, G. Dhiman and S. Kautish, "Deep learning model for the automatic classification of white blood cells," Computational Intelligence and Neuroscience, 2022.
  • [10] M. J. Macawile, V. V. Quiñones, A. Ballado, J. D. Cruz and M. V. Caya, "White blood cell classification and counting using convolutional neural network," 3rd International conference on control and robotics engineering (ICCRE), pp. 259-263, 2018.
  • [11] Y. Wang and Y. Cao, "Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation," Medical physics, vol. 47, no. 1, pp. 142-151, 2020.
  • [12] M. Sharma, A. Bhave and R. R. Janghel, "White blood cell classification using convolutional neural network," Soft Computing and Signal Processing , pp. 135-143, 2019.
  • [13] S. H. Rezatofighi and H. Soltanian-Zadeh, "Automatic recognition of five types of white blood cells in peripheral blood," Computerized Medical Imaging and Graphics, vol. 35, no. 4, pp. 333-343, 2011.
  • [14] M. Mohamed, B. Far and A. Guaily, "An efficient technique for white blood cells nuclei automatic segmentation," 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 220-225, 2012.
  • [15] O. Sarrafzadeh, H. Rabbani, A. Talebi and H. U. Banaem, "Selection of the best features for leukocytes classification in blood smear microscopic images," Medical Imaging 2014: Digital Pathology, vol. 9041, pp. 159-166, 2014.
  • [16] R. D. Labati, V. Piuri and F. Scotti, "All-IDB: The acute lymphoblastic leukemia image database for image processing," 18th IEEE international conference on image processing, pp. 2045-2048, 2011.
  • [17] X. Zheng, Y. Wang, G. Wang and J. Liu, "Fast and robust segmentation of white blood cell images by self-supervised learning," Micron, vol. 107, pp. 55-71, 2018.
  • [18] Z. M. Kouzehkanan et al., "A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm," Scientific reports, vol. 12, no. 1, pp. 1-14, 2022.
  • [19] J. Deng, W. Dong, R. Socher, L. J. Li, K. Li and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," IEEE conference on computer vision and pattern recognition, pp. 248-255, 2009.
  • [20] M. Yildirim and A. Çinar, "Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease," Rev. d'Intelligence Artif., vol. 33, no. 5, pp. 335-340, 2019.
  • [21] C. Jung, M. Abuhamad, J. Alikhanov, A. Mohaisen, K. Han and D. Nyang, "W-net: a CNN-based architecture for white blood cells image classification," arXiv preprint arXiv:1910.01091, 2019.
  • [22] A. Ekiz, K. Kaplan and H. M. Ertunç, "Classification of white blood cells using CNN and Con-SVM," 29th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2021.
  • [23] A. Girdhar, H. Kapur and V. Kumar, "Classification of White blood cell using Convolution Neural Network," Biomedical Signal Processing and Control, vol. 71, 2022.
There are 23 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Oğuzhan Katar 0000-0002-5628-3543

İlhan Fırat Kılınçer 0000-0001-8090-4998

Publication Date December 31, 2022
Submission Date October 31, 2022
Acceptance Date December 22, 2022
Published in Issue Year 2022

Cite

IEEE O. Katar and İ. F. Kılınçer, “Automatic Classification of White Blood Cells Using Pre-Trained Deep Models”, SAUCIS, vol. 5, no. 3, pp. 462–476, 2022, doi: 10.35377/saucis...1196934.

Cited By

Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model
Neural Computing and Applications
https://doi.org/10.1007/s00521-023-09158-9

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