This paper presents a novel method to detect three types of abnormal Red Blood Cells (RBCs) called Poikilocytes in Iron deficient blood smears. Classification and counting the number of Poikilocyte cells is considered as an important step for the automatic detection of Iron Deficiency Anemia (IDA) disease. Dacrocyte, Elliptocyte and Schistocyte cells are three essential Poikilocyte cells that are prevalent in IDA. The suggested cell recognition approach includes preprocessing, segmentation, feature extraction and classification steps.
Classification is done by using three distinct classifiers including Neural Network (NNET), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. Finally, the output of all of the three classifiers are used via Maximum Voting theory to choose the proper class. In maximum voting theory, the class that receives the maximum number of votes is chosen as the final predicted class of a sample cell. In this paper, the accuracy of the proposed method is %99, %97 and %100 for detecting Dacrocyte cells, Elliptocyte cells and Schistocyte cells, respectively.