Affiliations: [a] Department of Artificial Intelligence, Science and Research Branch, Islamic Azad University, Tehran, Iran | [b] Research Center for Biomedical Technology and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran | [c] Medical Radiation Engineering Department, Amirkabir University of Technology, Tehran, Iran
Correspondence:
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Corresponding author: Hossein Arabalibeik, Research Center for Biomedical Technology and Robotics (RCBTR), Tehran University of Medical Sciences, Tehran, Iran. E-mail: arabalibeik@tums.ac.ir.
Abstract: Volume of red blood cell is an important factor in distinguishing its abnormalities. Mean corpuscular volume (MCV) of red blood cells contributes much to differentiation of several blood diseases like iron deficiency and other types of anemia. This paper proposes an automated system to classify blood samples using cell microscopic images instead of pathology test results. Adaptive local thresholding is first used to segment cell images. The volumes of red cells are then estimated by assuming torus geometry for cells. Finally, an adaptive network-based fuzzy inference system (ANFIS) is used to classify blood samples to normal and abnormal. Accuracy of the proposed system and area under Receiver Operating Characteristics (ROC) curve are 100% and 1 respectively.
Keywords: Mean corpuscular volume (MCV), red blood cell, anemia, cell microscopic image, ANFIS
