Abstract

In the medical field, blood testing is considered one of the most important clinical examinations. A complete blood cell count is important for any medical diagnosis. Traditionally manual equipment is used to do this task which is time-consuming. Therefore, there is a need to research for an automated blood cell detection system that will help physicians to solve the problem efficiently. This paper presents a machine learning approach for the automatic identification and classification of three types of blood cells using a Single-shot Multi-Box detector (SSD) network. This framework has been trained on the BCCD Dataset of blood smear images to automatically identify red blood cells, White blood cells, and platelets.

References

• M. T. Islam, M. A. Aowal, A. T. Minhaz, “Abnormality detection and localization in chest X-rays using deep convolutional neural networks,” Med. Biol. Eng. Comput., 2017.
• M. R. Avendi, A. Kheradvar, H. Jafarkhani,“A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI,” Med. Image Anal., 2016.
• V. Gulshan, L. Peng, M. Coram, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” J. Am. Med, Assoc., 2016.
• J. Redmon, S. Divvala, R. Girshick, “You Only Look Once: unified, real-time object detection,” IEEE Conf. on Computer Vision and Pattern Recognition, December 2016.
• Wei Liu., Dragomir Anguelov., Dumitru Erhan, “SSD: Single Shot Multi-Box Detector,” December 2016.
• Mohammad Mahmudul Alam, “Machine learning approach of automatic identification and counting of blood cells”, Healthcare Technology Letters, 2018.
• Wang Shitong, Wang Min, “A new detection algorithm based on fuzzy cellular neural networks for white blood cell detection,” IEEE Transactions on Information Technology in Biomedicine, Vol.10, No. 1, January 2006.
• D. Cruz, C. Jennifer, L. C. Valiente, “Determination of blood components count in microscopic images using image processing and analysis,” Proc. Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, December 2017.
• Lou J., Zhou M., Li Q. “An automatic red blood cell counting method based on spectral images,” Proc. Int. Congress on Image and Signal Processing, Biomedical Engineering and Informatics, October 2016.
• V. Acharya, P. Kumar, “Identification and red blood cell automated counting from blood smear images using computer-aided system,” Med. Biol. Eng. Comput., 2018.
• J. Zhao, M. Zhang, Z. Zhou, “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput., 2017.
• P. Sobrevilla, E. Montseny, J. Keller, “White blood cell detection in bone marrow images,” 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).
• BCCD, Blood Cell Images. [Online]. Available: https://github.com/Shenggan/BCCD_Dataset.
• K. Simonyan, A. Zisserman, “Very deep convolutional networks for large scale image recognition,” Proc. Int. Conf. Learning Representations, San Diego, 2015.
• P. Kaur, V. Sharma, N. Garg, “Platelet count using image processing,” Int. Conf. on Computing for Sustainable Global Development, March 2016.