Skip to main navigation menu Skip to main content Skip to site footer

Diagnosing the Leukemia using Faster Region based Convolutional Neural Network


It is difficult to building deep learning algorithms for identifying chronic diseases. One of the must difficulties facing the system of diagnosing leukemia is the irregular shape and twisted nucleus in white blood cells (WBCs) without cleaning and segmentation of cells by Appling filters. Moreover, it is challenge to identify and classify the WBC at once time which is considered the essential step of leukemia diagnosing. This paper proposed system only based on deep learning algorithms. The modified Faster R-CNN (Faster Region based-Convolutional Neural Networks) algorithm is used to detect and classify WBCs. The system is achieved a high accuracy training on the database tacked directly from microscope which used in [4].



Faster Region CNN, White Blood Cells, Deep Learning, Classification, Detection


Author Biography

Shakir M. Abas




  1. E. M. Dogo, O. J. Afolabi, N. I. Nwulu, B. Twala, and C. O. Aigbavboa, “A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks,” in 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Dec. 2018, pp. 92–99. doi: 10.1109/CTEMS.2018.8769211.
  2. A. Bazaga, M. Roldán, C. Badosa, C. Jiménez-Mallebrera, and J. M. Porta, “A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies,” Appl. Soft Comput., vol. 85, p. 105772, Dec. 2019, doi: 10.1016/j.asoc.2019.105772.
  3. M. Liu et al., “A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease,” NeuroImage, vol. 208, p. 116459, Mar. 2020, doi: 10.1016/j.neuroimage.2019.116459.
  4. G. Li, Z. Song, and Q. Fu, “A New Method of Image Detection for Small Datasets under the Framework of YOLO Network,” in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Oct. 2018, pp. 1031–1035. doi: 10.1109/IAEAC.2018.8577214.
  5. S. Shafique and S. Tehsin, “Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks,” Technol. Cancer Res. Treat., vol. 17, p. 1533033818802789, Jan. 2018, doi: 10.1177/1533033818802789.
  6. B. Rasheed, “An Improved Novel ANN Model for Detection Of DDoS Attacks On Networks,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, pp. 9–16, Sep. 2019, doi: 10.30534/ijatcse/2019/0281.42019.
  7. S. Laddha, “Analysis of White Blood Cell Segmentation Techniques and Classification Using Deep Convolutional Neural Network for Leukemia Detection,” HELIX, vol. 8, pp. 4519–4524, Oct. 2018, doi: 10.29042/2018-4519-4524.
  8. T. Araújo et al., “Classification of breast cancer histology images using Convolutional Neural Networks,” PLOS ONE, vol. 12, no. 6, p. e0177544, Jun. 2017, doi: 10.1371/journal.pone.0177544.
  10. M. Claro et al., “Convolution Neural Network Models for Acute Leukemia Diagnosis,” in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Jul. 2020, pp. 63–68. doi: 10.1109/IWSSIP48289.2020.9145406.
  11. Y. N. Fu’adah, N. C. Pratiwi, M. A. Pramudito, and N. Ibrahim, “Convolutional Neural Network (CNN) for Automatic Skin Cancer Classification System,” IOP Conf. Ser. Mater. Sci. Eng., vol. 982, no. 1, p. 012005, Dec. 2020, doi: 10.1088/1757-899X/982/1/012005.
  12. J. Gao, Q. Jiang, B. Zhou, and D. Chen, “Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview,” Math. Biosci. Eng. MBE, vol. 16, no. 6, pp. 6536–6561, Jul. 2019, doi: 10.3934/mbe.2019326.
  13. J. Boulent, S. Foucher, J. Théau, and P.-L. St-Charles, “Convolutional Neural Networks for the Automatic Identification of Plant Diseases,” Front. Plant Sci., vol. 10, 2019, Accessed: Dec. 17, 2022. [Online]. Available:
  14. S. M. Abas and A. M. Abdulazeez, “Detection and Classification of Leukocytes in Leukemia using YOLOv2 with CNN,” in Asian Journal of Research in Computer Science, May 2021, pp. 64–75. doi: 10.9734/ajrcos/2021/v8i330204.
  15. P. Alves-Oliveira, P. Arriaga, A. Paiva, and G. Hoffman, “Guide to build YOLO, a creativity-stimulating robot for children,” HardwareX, vol. 6, p. e00074, Oct. 2019, doi: 10.1016/j.ohx.2019.e00074.
  16. L. Lin, W. Wang, and B. Chen, “Leukocyte recognition with convolutional neural network,” J. Algorithms Comput. Technol., vol. 13, p. 174830181881332, Jan. 2019, doi: 10.1177/1748301818813322.
  17. M. M. Alam and M. T. Islam, “Machine learning approach of automatic identification and counting of blood cells,” Healthc. Technol. Lett., vol. 6, no. 4, pp. 103–108, Jul. 2019, doi: 10.1049/htl.2018.5098.
  18. N. Ghane, A. Vard, A. Talebi, and P. Nematollahy, “Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm,” J. Med. Signals Sens., vol. 7, no. 2, pp. 92–101, 2017.
  19. M. Togacar, B. Ergen, and M. E. Sertkaya, “Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models,” Elektron. Ir Elektrotechnika, vol. 25, no. 5, Art. no. 5, Oct. 2019, doi: 10.5755/j01.eie.25.5.24358.
  20. R. R. Tobias et al., “Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification,” in 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), Mar. 2020, pp. 235–239. doi: 10.1109/LifeTech48969.2020.1570619208.
  21. S.-J. Lee, P.-Y. Chen, and J.-W. Lin, “Complete Blood Cell Detection and Counting Based on Deep Neural Networks,” Appl. Sci., vol. 12, no. 16, Art. no. 16, Jan. 2022, doi: 10.3390/app12168140.
  22. J. Hung et al., “Applying Faster R-CNN for Object Detection on Malaria Images.” arXiv, Mar. 11, 2019. doi: 10.48550/arXiv.1804.09548.
  23. S. M. Abas, A. M. Abdulazeez, and D. Q. Zeebaree, “A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia,” Indones. J. Electr. Eng. Comput. Sci., vol. 25, no. 1, Art. no. 1, Jan. 2022, doi: 10.11591/ijeecs.v25.i1.pp200-213.
  24. N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud, “An Automated Blood Cells Counting and Classification Framework using Mask R-CNN Deep Learning Model,” in 2019 31st International Conference on Microelectronics (ICM), Dec. 2019, pp. 300–303. doi: 10.1109/ICM48031.2019.9021862.
  25. L. Alzubaidi, M. A. Fadhel, O. Al-Shamma, J. Zhang, and Y. Duan, “Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis,” Electronics, vol. 9, no. 3, Art. no. 3, Mar. 2020, doi: 10.3390/electronics9030427.


Metrics Loading ...