Classification of Red Blood Cell Shapes Using a Sequential Learning Algorithm

Abstract

[eng] This paper presents a method that uses a sequential representation to train Hidden Markov Models as an algorithm for the supervised morphological classification of erythrocytes in peripheral blood samples from patients with sickle cell anemia, considering three classes: circular, elongated and with others deformations. This sequential learning method provides the probability of belonging the object to the class and for the representation of the red cell contour, characteristics are not obtained, but the contour is analyzed as a sequence of curvatures. The experimentation carried out analyzes each group as a class and considers 3, 8, 9, 10 and 11 states, so that the method is capable of dealing with the local angular differences existing in this representation, with the aim of improving the performance of the classification obtained so far. To check the effectiveness of this method, we use samples with balanced classes formed by images of individual erythrocytes, in similar amounts for each of the three classes. Measurements of sensitivity, precision, specificity, F1 and classification accuracy were obtained. The best results were obtained for the representation considering 10 states.