Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

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Ishwari Singh Rajput 1 Sonam Tyagi 1 Aditya Gupta 2,* Vibha Jain 3

1. School of Computing, Graphic Era Hill University, Haldwani, India

2. Thapar Institute of Engineering and Technology, Patiala, India

3. Chitkara University Institute of Engineering and Technology,Chitkara University, Punjab, India

* Corresponding author.


Received: 21 Jan. 2023 / Revised: 6 Feb. 2023 / Accepted: 10 Apr. 2023 / Published: 8 Feb. 2024

Index Terms

Deep Learning, CNN, Classification, White Blood Cells classification, Feature extraction


White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.

Cite This Paper

Ishwari Singh Rajput, Sonam Tyagi, Aditya Gupta, Vibha Jain, "Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.1, pp. 108-125, 2024. DOI:10.5815/ijigsp.2024.01.08


[1]Q. Wang, L. Chang, M. Zhou, Q. Li, H. Liu, and F. Guo, “A spectral and morphologic method for white blood cell classification,” Opt Laser Technol, vol. 84, pp. 144–148, 2016.
[2]A. Şengür, Y. Akbulut, Ü. Budak, and Z. Cömert, “White blood cell classification based on shape and deep features,” in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1–4.
[3]E. Cengil, A. Çnar, and M. Yldrm, “A hybrid approach for efficient multi-classification of white blood cells based on transfer learning techniques and traditional machine learning methods,” Concurr Comput, vol. 34, no. 6, p. e6756, 2022.
[4]N. M. Deshpande, S. Gite, B. Pradhan, K. Kotecha, and A. Alamri, “Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.,” Math Biosci Eng, 2022.
[5]A. Myari, E. Papapetrou, and C. Tsaousi, “Diagnostic value of white blood cell parameters for COVID-19: Is there a role for HFLC and IG?,” Int J Lab Hematol, vol. 44, no. 1, pp. 104–111, 2022.
[6]H. W. Loh, C. P. Ooi, S. Seoni, P. D. Barua, F. Molinari, and U. R. Acharya, “Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022),” Comput Methods Programs Biomed, p. 107161, 2022.
[7]O. Ali, W. Abdelbaki, A. Shrestha, E. Elbasi, M. A. A. Alryalat, and Y. K. Dwivedi, “A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities,” Journal of Innovation & Knowledge, vol. 8, no. 1, p. 100333, 2023.
[8]R. Patil and K. Shah, “Machine Learning in Healthcare: Applications, Current Status, and Future Prospects,” Handbook of Research on Machine Learning, pp. 163–186, 2023.
[9]M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Rab, “Significance of machine learning in healthcare: Features, pillars and applications,” International Journal of Intelligent Networks, vol. 3, pp. 58–73, 2022.
[10]M. A. Talukder, M. M. Islam, M. A. Uddin, A. Akhter, K. F. Hasan, and M. A. Moni, “Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning,” Expert Syst Appl, vol. 205, p. 117695, 2022.
[11]S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometrics recognition using deep learning: A survey,” Artif Intell Rev, pp. 1–49, 2023.
[12]A. Kamble, P. H. Ghare, and V. Kumar, “Deep-Learning-Based BCI for Automatic Imagined Speech Recognition Using SPWVD,” IEEE Trans Instrum Meas, vol. 72, p. 4001110, 2023.
[13]M. Anand, K. B. Sahay, M. A. Ahmed, D. Sultan, R. R. Chandan, and B. Singh, “Deep learning and natural language processing in computation for offensive language detection in online social networks by feature selection and ensemble classification techniques,” Theor Comput Sci, vol. 943, pp. 203–218, 2023.
[14]S. Saravanan, K. Surendheran, and K. Krishnakumar, “Data Analytics on Medical Images with Deep Learning Approach,” in Biomedical Signal and Image Processing with Artificial Intelligence, Springer, 2023, pp. 153–166.
[15]L. Xie et al., “Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation,” Med Image Anal, vol. 83, p. 102683, 2023.
[16]B. Hemalatha, B. Karthik, C. V. K. Reddy, and A. Latha, “Deep learning approach for segmentation and classification of blood cells using enhanced CNN,” Measurement: Sensors, vol. 24, p. 100582, 2022.
[17]M. Toğaçar, B. Ergen, and Z. Cömert, “Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods,” Appl Soft Comput, vol. 97, p. 106810, 2020.
[18]P. K. Das and S. Meher, “An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia,” Expert Syst Appl, vol. 183, p. 115311, 2021.
[19]A. M. Patil, M. D. Patil, and G. K. Birajdar, “White blood cells image classification using deep learning with canonical correlation analysis,” Irbm, vol. 42, no. 5, pp. 378–389, 2021.
[20]Y. Lu, X. Qin, H. Fan, T. Lai, and Z. Li, “WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet,” Appl Soft Comput, vol. 101, p. 107006, 2021.
[21]H. Kutlu, E. Avci, and F. Özyurt, “White blood cells detection and classification based on regional convolutional neural networks,” Med Hypotheses, vol. 135, p. 109472, 2020.
[22]A. Girdhar, H. Kapur, and V. Kumar, “Classification of white blood cell using convolution neural network,” Biomed Signal Process Control, vol. 71, p. 103156, 2022.
[23]P. P. Banik, R. Saha, and K.-D. Kim, “An automatic nucleus segmentation and CNN model based classification method of white blood cell,” Expert Syst Appl, vol. 149, p. 113211, 2020.
[24]A. Sahu, K. P. S. Rana, and V. Kumar, “An application of deep dual convolutional neural network for enhanced medical image denoising,” Med Biol Eng Comput, pp. 1–14, 2023.
[25]R. Venkatesan and P. Umamaheswari, “Automatic Classification of Diseases From X-Ray Images Using Xception Deep Convolution Neural Networks,” in Using Multimedia Systems, Tools, and Technologies for Smart Healthcare Services, IGI Global, 2023, pp. 176–190.
[26]Y.-T. Lin, B.-C. Shia, C.-J. Chang, Y. Wu, J.-D. Yang, and J.-H. Kang, “Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis,” J Digit Imaging, pp. 1–9, 2023.
[27]Y. Zhang, J. Yi, A. Chen, and L. Cheng, “Cardiac arrhythmia classification by time–frequency features inputted to the designed convolutional neural networks,” Biomed Signal Process Control, vol. 79, p. 104224, 2023.
[28]M. K. Chegeni, A. Rashno, and S. Fadaei, “Convolution-layer parameters optimization in Convolutional Neural Networks,” Knowl Based Syst, vol. 261, p. 110210, 2023.
[29]R. Ali, J. H. Chuah, M. S. A. Talip, N. Mokhtar, and M. A. Shoaib, “Structural crack detection using deep convolutional neural networks,” Autom Constr, vol. 133, p. 103989, 2022.
[30]I. A. Bratchenko, L. A. Bratchenko, Y. A. Khristoforova, A. A. Moryatov, S. V Kozlov, and V. P. Zakharov, “Classification of skin cancer using convolutional neural networks analysis of Raman spectra,” Comput Methods Programs Biomed, vol. 219, p. 106755, 2022.
[31]Ö. Gültekin, E. Çinar, K. Özkan, and A. Yazc, “A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images,” Neural Comput Appl, vol. 34, no. 6, pp. 4803–4812, 2022.
[32]S. Guo, G. Wang, L. Han, X. Song, and W. Yang, “COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter,” Biomed Signal Process Control, vol. 75, p. 103552, 2022.
[33]D. O. Oyewola, E. G. Dada, S. Misra, and R. Damaševičius, “A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images,” Applied Artificial Intelligence, vol. 36, no. 1, p. 2033473, 2022.
[34]A. Zarei, H. Beheshti, and B. M. Asl, “Detection of sleep apnea using deep neural networks and single-lead ECG signals,” Biomed Signal Process Control, vol. 71, p. 103125, 2022.
[35]J. Alyami et al., “Tumor Localization and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm,” Cognit Comput, pp. 1–11, 2023.
[36]R. H. F. Alves, G. A. de Deus Junior, E. G. Marra, and R. P. Lemos, “Automatic fault classification in photovoltaic modules using Convolutional Neural Networks,” Renew Energy, vol. 179, pp. 502–516, 2021.