RBC Classification in Blood Smear Image using Neural Network
Debaraj Rana1, Sushanta Kumar Sahu2
1Debaraj Rana*, Department of ECE, Centurion University of Technology and Management, Bhubaneswar Campus, Bhubaneswar, Odisha, , India.
2Sushanta Kumar Sahu, Department of IEE, College of Engineering and Technology (CET), Bhubaneswar, Odisha, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 26, 2020. | Manuscript published on March 10, 2020. | PP: 2114-2118 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2991039520/2020©BEIESP | DOI: 10.35940/ijitee.E2991.039520
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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: Biomedical image processing becomes an emerging field due to automation in the field of medical science with the help of image processing techniques. In medical science it is very much essential to diagnosis a disease accurately and efficiently. Most of the disease which deals with the blood test report for diagnosis of the disease. This paper proposed a computer vision based method which extract the Red Blood Cells (RBC) from a blood smear image and classify it whether normal or abnormal. Then it will count the normal RBC as well as abnormal RBC. This method works in two parts, one is segmentation of blood cell and other is classification and counting of segmented blood cells using neural network. The Neural network trained and classified using shape and moment invariant features because this features are invariant to translation, scaling and rotation. The proposed method performs well and gives about 90 percent of correct result.
Keywords: Biomedical, Neural Network, Morphology, RBC
Scope of the Article: Biomedical Computing