AIMS Bioengineering, 2018, 5(3): 179-191. doi: 10.3934/bioeng.2018.3.179.

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Red and white blood cell classification using Artificial Neural Networks

1 Histology and Embryology Department Doctoral Program, Yıldırım Beyazıt University
2 Graduate School of Informatics, Middle East Technical University

Blood cell classification is a recent topic for scientists working on the diagnosis of blood cell related illnesses. As the number of computer vision (CV) applications is increasing to improve quality of human life, it spreads in the areas of autonomous drive, surveillance, robotic applications, telecommunications and etc. The number of CV applications also increases in the medical sector due the decreasing value of doctors per patient ratio (DPPR) in urban and suburban areas. A doctor working in such areas sometimes would have to interpret thousands of patients’ test results in a day. This condition would result disadvantages such as false diagnosis on patients and break on working motivations for doctors. Some of the tests would probably be interpreted using an application developed by Artificial Neural Networks (ANN). Tests related to blood cells are examined for the patients as a starting point of diagnosis and information obtained about their abnormalities give doctors a preliminary idea about the illnesses. This article issues generation of a CV application that would be used as an assistant of doctors who have domain expertise. The article issues segmentation of blood cells, classification of red and white blood cells containing 6 types such as erythrocyte, lymphocyte, platelets, neutrophil, monocytes and eosinophils using the segmentation results. It also discusses about a method for detection of abnormalities on red blood cells (erythrocyte).
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Keywords Blood cell classification; Artificial Neural Networks; peripheral blood smear; blood cell abnormalities

Citation: Simge Çelebi, Mert Burkay Çöteli. Red and white blood cell classification using Artificial Neural Networks. AIMS Bioengineering, 2018, 5(3): 179-191. doi: 10.3934/bioeng.2018.3.179


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