Detection of Subtype Blood Cells using Deep Learning

Prayag Tiwari, Jia Qian, Qiuchi Li, Benyou Wang, Deepak Gupta, Ashish Khanna, Joel J.P.C. Rodrigues*, Victor Hugo C. de Albuquerque

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Deep Learning has already shown power in many application fields, and is accepted by more and more people as a better approach than the traditional machine learning models. In particular, the implementation of deep learning algorithms, especially Convolutional Neural Networks (CNN), brings huge benefits to the medical field, where a huge number of images are to be processed and analyzed. This paper aims to develop a deep learning model to address the blood cell classification problem, which is one of the most challenging problems in blood diagnosis. A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells. Experiments are conducted on a dataset of 13k images of blood cells with their subtypes, and the results show that our proposed model provide better results in terms of evaluation parameters

Original languageEnglish
JournalCognitive Systems Research
Pages (from-to)1036-1044
Publication statusPublished - 2018


  • Blood Cells
  • Classification
  • CNN

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