An Efficient Convolutional Neural Network to Detect and Count Blood Cells

Keywords: convolutional neural network, deep learning, platelets, red blood cells, white blood cells


Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in low-cost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.


Acharjee, S., Chakrabartty, S., Alam, M. I., Dey, N., Santhi, V. & Ashour, A. S. (2016). A semiautomated approach using GUI for the detection of red blood cells. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 525–529.
Acharya, V. & Kumar, P. (2018). Identification and red blood cell automated counting from blood smear images using computer-aided system. Medical & Biological Engineering & Computing, 56(3), 483–489.
Alam, M. M. & Islam, M. T. (2019). Machine learning approach of automatic identification and counting of blood cells. Healthcare Technology Letters, 6(4), 103–108.
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Van Esesn, B. C., Awwal, A. A. S. & Asari, V. K. (2018). The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Alomari, Y. M., Sheikh Abdullah, S. N. H., Zaharatul Azma, R. & Omar, K. (2014). Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm. Computational and Mathematical Methods in Medicine, 2014, 1–17.
BCCD. (2020). Blood Cell Count Dataset. GitHub, Inc.
Cruz, D., Jennifer, C., Valiente, Castor, L. C., Mendoza, C. M. T., Jay, B. A., Jane, L. S. C. & Brian, P. T. B. (2017). Determination of blood components (WBCs, RBCs, and Platelets) count in microscopic images using image processing and analysis. 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 1–7.
Habibzadeh Motlagh, M., Jannesari, M., Rezaei, Z., Totonchi, M. & Baharvand, H. (2018). Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception. In J. Zhou, P. Radeva, D. Nikolaev & A. Verikas (Eds.), Tenth International Conference on Machine Vision (ICMV 2017) (p. 105). SPIE.
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.
LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B. & Belongie, S. (2017). Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936–944.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context (pp. 740–755). In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham.
Lou, J., Zhou, M., Li, Q., Yuan, C. & Liu, H. (2016). An automatic red blood cell counting method based on spectral images. 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1391–1396.
Ma, Rong., Liang, Yuanzhi. & Ma, Y. (2016). A self-adapting method for RBC count from different blood smears based on PCNN and image quality. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1611–1615.
Sarrafzadeh, O., Dehnavi, A. M., Rabbani, H., Ghane, N. & Talebi, A. (2015). Circlet based framework for red blood cells segmentation and counting. 2015 IEEE Workshop on Signal Processing Systems (SiPS), 1–6.
How to Cite
Joshi, R., Yadav, S., Dutta, M., & Travieso-González, C. (2022). An Efficient Convolutional Neural Network to Detect and Count Blood Cells. Uniciencia, 36(1), 1-11.
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