Abnormal Red Blood Cells Detection Using Adaptive Neuro-fuzzy System
Publish place: The 14th Annual Conference and the International Congress of Pathology and Laboratory Medicine
Publish Year: 1391
Type: Conference paper
Language: English
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Document National Code:
ACPLMED14_059
Index date: 11 November 2018
Abnormal Red Blood Cells Detection Using Adaptive Neuro-fuzzy System abstract
Red blood cells features like size, shape, and volume are important factors in diagnosingrelated blood disorders like iron deficiency and anemia. This paper proposes a method todetect abnormality in red blood cells using cell microscopic images. Adaptive localthresholding and bounding box methods are used to extract inner and outer diameters of redcells. An adaptive network-based fuzzy inference system (ANFIS) is used to classify bloodsamples to normal and abnormal. Accuracy of the proposed method and area under ROCcurve are 96.6% and 0.9950 respectively.
Abnormal Red Blood Cells Detection Using Adaptive Neuro-fuzzy System Keywords:
Abnormal Red blood cells , anemia , microscopic images , adaptive local thresholding , adaptive neuro-fuzzy system
Abnormal Red Blood Cells Detection Using Adaptive Neuro-fuzzy System authors
N Babazadeh Khameneh
Department of Artificial Intelligence, Science and Research Branch, Islamic Azad University, Tehran, Iran
H Arabalibeik
Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran
P Salehianc
Sarem Cell Research Center (SCRC), Sarem women s Hospital, Tehran, Iran
S Setayeshi
Medical Radiation Department, Amirkabir University of Technology, Tehran, Iran