Classifying White Blood Cells in Blood Smear Images using a Convolutional Neural Network
Gulshan Sharma1, Rakesh Kumar2
1Gulshan Sharma, Department of Computer Science & Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India.
2Rakesh Kumar, Department of Computer Science & Engineering, National Institute of Technical Teachers Training & Research, Chandigarh, India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 825-829 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11330789S19/19©BEIESP | DOI: 10.35940/ijitee.I1133.0789S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We have tried to automate the classification task of white blood cells by using a Convolutional Neural Network. We have divided white blood cell classification in two types of problems, a binary class problem and a 4-classification problem. In binary class problem we classify white blood cell as either mononuclear or Grenrecules. In 4-classification problem where cells are classified into their subtypes (monocytes, lymphocytes, neutrophils, basophils and eosinophils). In our experiment we were able to achieve validation accuracy of 100% in binary classification and 98.40 in multiple classifications.
Keywords: Convolutional Neural Network, Deep Learning, Medical Diagnosis, White Blood Cell Classification.
Scope of the Article: Classification