Research Article
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Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network

Year 2021, Volume: 2 Issue: 2, 81 - 88, 21.12.2021
https://doi.org/10.53525/jster.1014186

Abstract

Classification of white blood cells plays a significant role in the detection of diseases which are infections caused by abnormalities in the immune system, allergies, anemia, leukemia, cancer, AIDS, etc. In traditional methods, experts manually examine the white blood cells under the microscope, and since this process is tedious, takes more time and can be more error-prone, automated systems have become necessary. Making this classification automatically will help experts in the detection of diseases. In this study, blood cells are classified from blood cell images using dense convolutional neural network. This paper intends to utilize a dense convolutional neural network model to overcome the blood cell classification problem that is one of the most compelling problems in blood diagnosis. In this study, a DenseNet121 model is built to classify blood cell images. Experiments are conducted on an open-access BCCD dataset (12507 white blood cell images that contain four types of white blood cells). Performance evaluations are performed on the accuracy of the techniques, and the results are compared with state-of-the-art deep learning-based approaches as Xception, VGG19, EfficientNetB1. In experimental studies, the highest accuracy (94%) is obtained with the proposed DenseNet121 model. Considering the high accuracy value obtained with this model, automatic detection of which class the cells belong to will speed up the diagnosis and allow more data to be examined by doctors.

References

  • [1] W. Stock, R. Hoffman, “White blood cells 1: non-malignant disorders,” The Lancet 355 (2000) 1351–1357, https://doi.org/10.1016/S0140-6736(00)02125-5.
  • [2] B. Medical, “Medical gallery of blausen medical 2014,” WikiJournal of Medicine, vol. 1, no. 2, pp. 1–79, 2014.
  • [3] Q. Wang, J. Wang, M. Zhou, Q. Li, Y. Wen, and J. Chu, “A 3D attention networks for classification of white blood cells from microscopy hyperspectral images,” Optics & Laser Technology, vol. 139, Article ID 106931, 2021
  • [4] M.S. Blumenreich, “The White Blood Cell and Differential Count,” in Clinical Methods: The History, Physical, and Laboratory Examinations, H. K. Walker, W. D. Hall, and J. W. Hurst, Eds., ed Boston: Butterworths Copyright © 1990, Butterworth Publishers, a division of Reed Publishing., 1990.
  • [5] B.J. Bain, “Diagnosis from the blood smear,” N Engl J Med 353 (2005) 498–507.
  • [6] S. Khan, M. Sajjad, T. Hussain, A. Ullah, and A. S. Imran, “A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images,” IEEE Access, vol. 9, Article ID 10657, 2020.
  • [7] P. Mooney, “Blood cell images,” https://www.kaggle.com/paultimothymooney/blood-cells, Accessed 22/10/2020.
  • [8] G. Liang, H. Hong, W. Xie, & L. Zheng, “Combining convolutional neural network with recursive neural network for blood cell image classification,” IEEE Access, 6, 36188-36197, 2018.
  • [9] D. Bani-Hani, N. Khan, F. Alsultan, S. Karanjkar, & N. Nagarur, “Classification of leucocytes using convolutional neural network optimized through genetic algorithm,” In Proc. of the 7th Annual World Conference of the Society for Industrial and Systems Engineering, 2018.
  • [10] A. Şengür, Y. Akbulut, Ü. Budak, & Z. Cömert, “White blood cell classification based on shape and deep features,” In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-4, IEEE, 2019.
  • [11] P.P. Banik, R. Saha, K. Kim, “Fused Convolutional Neural Network for White Blood Cell Image Classification,” in 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, pp. 238-240. Doi: 10.1109/ICAIIC.2019.8669049.
  • [12] A. M. Patil, M. D. Patil, & G. K. Birajdar, “White blood cells image classification using deep learning with canonical correlation analysis,” IRBM, 2020.
  • [13] E. H Mohamed, W. H El-Behaidy, G. Khoriba, & J. Li, “Improved White Blood Cells Classification based on Pre-trained Deep Learning Models,” Journal of Communications Software and Systems, 16(1), 37-45, 2020.
  • [14] O. Dekhil, “Computational techniques in medical image analysis application for white blood cells classification,” Electronic Theses and Dissertations. Paper 3424, 2020.
  • [15] I. Ghosh & S. Kundu, “Combining Neural Network Models for Blood Cell Classification,” arXiv preprint arXiv:2101.03604, 2021.
  • [16] A. Ekiz, K. Kaplan, & H. M. Ertunç, “Classification of White Blood Cells Using CNN and Con-SVM,” In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE, 2021.
  • [17] X. Li, W. Li, X. Xu, and W. Hu, "Cell classification using convolutional neural networks in medical hyperspectral imagery,” 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, 2017.
  • [18] W. Yu, J. Chang, C. Yang, L. Zhang, H. Shen, Y. Xia, and J. Sha, "Automatic classification of leukocytes using deep neural network," IEEE 12th International Conference on ASIC (ASICON), Guiyang, China, 2017.
  • [19] M. Jiang, L. Cheng, F. Qin, L. Du, and M. Zhang, "White blood cells classification with deep convolutional neural networks," International Journal of Pattern Recognition and Artificial Intelligence, 32 (9): 1857006, 2018.
  • [20] S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, & M. K. Khan, “Medical image analysis using convolutional neural networks: a review,” Journal of medical systems, 42(11), 1-13, 2018.
  • [21] A.I. Shahin, T. Guo, K. M. Amin, and A. A. Sharawi, “White blood cells identification system based on convolutional deep neural learning networks,” Computer Methods and Programs in Biomedicine, 168:69-80, 2019.
  • [22] K. Throngnumchai, P. Lomvisai, A. Tantasirin and P. Phasukkit, "Classification of White blood cell using Deep Convolutional Neural Network," Biomedical Engineering International Conference (BMEiCON), 12:1-4, 2019.
  • [23] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
  • [24] S. Dargan, M. Kumar, M. R. Ayyagari, & G. Kumar, “A survey of deep learning and its applications: a new paradigm to machine learning,” Archives of Computational Methods in Engineering, 27(4), 1071-1092, 2020.
  • [25] E. Uzundurukan & A. Kara, “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation,” Journal of Scientific, Technology and Engineering Research, 1(2), 4-12, 2020.
  • [26] P. Kim, “Convolutional neural network. In MATLAB deep learning,” pp. 121-147, Apress, Berkeley, CA, 2017.
  • [27] L. Sarker, M. Islam, T. Hannan, and A. Zakaria, “COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images,” Preprints, 2020.
  • [28] S. Kumar, S. Mishra, and S. K. Singh, “Deep Transfer Learning-based COVID-19 prediction using Chest X-rays,” medRxiv, 2020.
  • [29] F. Bozkurt and M.Yağanoğlu, “COVID-19 detection from chest X-Ray images using dense convolutional network,” International Syposium on Applied Sciences and Engineering (ISASE2021), Erzurum, Turkey, 2021.

Dense Evrişimsel Ağ Kullanarak Kan Hücresi Görüntülerinden Kan Hücrelerinin Sınıflandırılması

Year 2021, Volume: 2 Issue: 2, 81 - 88, 21.12.2021
https://doi.org/10.53525/jster.1014186

Abstract

Classification of white blood cells plays a significant role in the detection of diseases which are infections caused by abnormalities in the immune system, allergies, anemia, leukemia, cancer, AIDS, etc. In traditional methods, experts manually examine the white blood cells under the microscope, and since this process is tedious, takes more time and can be more error-prone, automated systems have become necessary. Making this classification automatically will help experts in the detection of diseases. In this study, blood cells are classified from blood cell images using dense convolutional neural network. This paper intends to utilize a dense convolutional neural network model to overcome the blood cell classification problem that is one of the most compelling problems in blood diagnosis. In this study, a DenseNet121 model is built to classify blood cell images. Experiments are conducted on an open-access BCCD dataset (12507 white blood cell images that contain four types of white blood cells). Performance evaluations are performed on the accuracy of the techniques, and the results are compared with state-of-the-art deep learning-based approaches as Xception, VGG19, EfficientNetB1. In experimental studies, the highest accuracy (94%) is obtained with the proposed DenseNet121 model. Considering the high accuracy value obtained with this model, automatic detection of which class the cells belong to will speed up the diagnosis and allow more data to be examined by doctors.

References

  • [1] W. Stock, R. Hoffman, “White blood cells 1: non-malignant disorders,” The Lancet 355 (2000) 1351–1357, https://doi.org/10.1016/S0140-6736(00)02125-5.
  • [2] B. Medical, “Medical gallery of blausen medical 2014,” WikiJournal of Medicine, vol. 1, no. 2, pp. 1–79, 2014.
  • [3] Q. Wang, J. Wang, M. Zhou, Q. Li, Y. Wen, and J. Chu, “A 3D attention networks for classification of white blood cells from microscopy hyperspectral images,” Optics & Laser Technology, vol. 139, Article ID 106931, 2021
  • [4] M.S. Blumenreich, “The White Blood Cell and Differential Count,” in Clinical Methods: The History, Physical, and Laboratory Examinations, H. K. Walker, W. D. Hall, and J. W. Hurst, Eds., ed Boston: Butterworths Copyright © 1990, Butterworth Publishers, a division of Reed Publishing., 1990.
  • [5] B.J. Bain, “Diagnosis from the blood smear,” N Engl J Med 353 (2005) 498–507.
  • [6] S. Khan, M. Sajjad, T. Hussain, A. Ullah, and A. S. Imran, “A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images,” IEEE Access, vol. 9, Article ID 10657, 2020.
  • [7] P. Mooney, “Blood cell images,” https://www.kaggle.com/paultimothymooney/blood-cells, Accessed 22/10/2020.
  • [8] G. Liang, H. Hong, W. Xie, & L. Zheng, “Combining convolutional neural network with recursive neural network for blood cell image classification,” IEEE Access, 6, 36188-36197, 2018.
  • [9] D. Bani-Hani, N. Khan, F. Alsultan, S. Karanjkar, & N. Nagarur, “Classification of leucocytes using convolutional neural network optimized through genetic algorithm,” In Proc. of the 7th Annual World Conference of the Society for Industrial and Systems Engineering, 2018.
  • [10] A. Şengür, Y. Akbulut, Ü. Budak, & Z. Cömert, “White blood cell classification based on shape and deep features,” In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-4, IEEE, 2019.
  • [11] P.P. Banik, R. Saha, K. Kim, “Fused Convolutional Neural Network for White Blood Cell Image Classification,” in 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, pp. 238-240. Doi: 10.1109/ICAIIC.2019.8669049.
  • [12] A. M. Patil, M. D. Patil, & G. K. Birajdar, “White blood cells image classification using deep learning with canonical correlation analysis,” IRBM, 2020.
  • [13] E. H Mohamed, W. H El-Behaidy, G. Khoriba, & J. Li, “Improved White Blood Cells Classification based on Pre-trained Deep Learning Models,” Journal of Communications Software and Systems, 16(1), 37-45, 2020.
  • [14] O. Dekhil, “Computational techniques in medical image analysis application for white blood cells classification,” Electronic Theses and Dissertations. Paper 3424, 2020.
  • [15] I. Ghosh & S. Kundu, “Combining Neural Network Models for Blood Cell Classification,” arXiv preprint arXiv:2101.03604, 2021.
  • [16] A. Ekiz, K. Kaplan, & H. M. Ertunç, “Classification of White Blood Cells Using CNN and Con-SVM,” In 2021 29th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE, 2021.
  • [17] X. Li, W. Li, X. Xu, and W. Hu, "Cell classification using convolutional neural networks in medical hyperspectral imagery,” 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China, 2017.
  • [18] W. Yu, J. Chang, C. Yang, L. Zhang, H. Shen, Y. Xia, and J. Sha, "Automatic classification of leukocytes using deep neural network," IEEE 12th International Conference on ASIC (ASICON), Guiyang, China, 2017.
  • [19] M. Jiang, L. Cheng, F. Qin, L. Du, and M. Zhang, "White blood cells classification with deep convolutional neural networks," International Journal of Pattern Recognition and Artificial Intelligence, 32 (9): 1857006, 2018.
  • [20] S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, & M. K. Khan, “Medical image analysis using convolutional neural networks: a review,” Journal of medical systems, 42(11), 1-13, 2018.
  • [21] A.I. Shahin, T. Guo, K. M. Amin, and A. A. Sharawi, “White blood cells identification system based on convolutional deep neural learning networks,” Computer Methods and Programs in Biomedicine, 168:69-80, 2019.
  • [22] K. Throngnumchai, P. Lomvisai, A. Tantasirin and P. Phasukkit, "Classification of White blood cell using Deep Convolutional Neural Network," Biomedical Engineering International Conference (BMEiCON), 12:1-4, 2019.
  • [23] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
  • [24] S. Dargan, M. Kumar, M. R. Ayyagari, & G. Kumar, “A survey of deep learning and its applications: a new paradigm to machine learning,” Archives of Computational Methods in Engineering, 27(4), 1071-1092, 2020.
  • [25] E. Uzundurukan & A. Kara, “Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation,” Journal of Scientific, Technology and Engineering Research, 1(2), 4-12, 2020.
  • [26] P. Kim, “Convolutional neural network. In MATLAB deep learning,” pp. 121-147, Apress, Berkeley, CA, 2017.
  • [27] L. Sarker, M. Islam, T. Hannan, and A. Zakaria, “COVID-DenseNet: A Deep Learning Architecture to Detect COVID-19 from Chest Radiology Images,” Preprints, 2020.
  • [28] S. Kumar, S. Mishra, and S. K. Singh, “Deep Transfer Learning-based COVID-19 prediction using Chest X-rays,” medRxiv, 2020.
  • [29] F. Bozkurt and M.Yağanoğlu, “COVID-19 detection from chest X-Ray images using dense convolutional network,” International Syposium on Applied Sciences and Engineering (ISASE2021), Erzurum, Turkey, 2021.
There are 29 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Ferhat Bozkurt 0000-0003-0088-5825

Publication Date December 21, 2021
Submission Date October 24, 2021
Acceptance Date November 14, 2021
Published in Issue Year 2021 Volume: 2 Issue: 2

Cite

APA Bozkurt, F. (2021). Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network. Journal of Science, Technology and Engineering Research, 2(2), 81-88. https://doi.org/10.53525/jster.1014186
AMA Bozkurt F. Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network. JSTER. December 2021;2(2):81-88. doi:10.53525/jster.1014186
Chicago Bozkurt, Ferhat. “Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network”. Journal of Science, Technology and Engineering Research 2, no. 2 (December 2021): 81-88. https://doi.org/10.53525/jster.1014186.
EndNote Bozkurt F (December 1, 2021) Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network. Journal of Science, Technology and Engineering Research 2 2 81–88.
IEEE F. Bozkurt, “Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network”, JSTER, vol. 2, no. 2, pp. 81–88, 2021, doi: 10.53525/jster.1014186.
ISNAD Bozkurt, Ferhat. “Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network”. Journal of Science, Technology and Engineering Research 2/2 (December 2021), 81-88. https://doi.org/10.53525/jster.1014186.
JAMA Bozkurt F. Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network. JSTER. 2021;2:81–88.
MLA Bozkurt, Ferhat. “Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network”. Journal of Science, Technology and Engineering Research, vol. 2, no. 2, 2021, pp. 81-88, doi:10.53525/jster.1014186.
Vancouver Bozkurt F. Classification of Blood Cells from Blood Cell Images Using Dense Convolutional Network. JSTER. 2021;2(2):81-8.

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