Assessing Malaria using Neutral Zone Classifiers with Mixture Discriminant Analysis on 2D Images of Red Blood Cells

We aim to build a classifier to distinguish between malaria-infected red blood cells (RBCs) and healthy cells using the two-dimensional (2D) microscopic images of RBCs. We demonstrate the process of cell segmentation and feature extraction from the 2D images.

 We describe an approach to address the problem using mixture discriminant analysis (MDA) on the 2D image profiles of the RBCs. The extracted features are used with Gaussian MDA to distinguish between healthy and malaria infected cells. We also use the neutral zone classifiers where ambiguous cases are identified separately by the classifier.

We compare the classification results from the regular classifiers such as linear discriminant analysis (LDA) or MDA and the methods where neutral zone classifiers are used. We see that including the neutral zone improves the classification results by controlling the false positive and false negatives. The number of misclassifications are seen to be lower than the case without neutral zone classifiers.

This paper presents an alternative approach for classification by incorporating neutral zone classifier approach, where a prediction is not made for the ambiguous cases. From the data analysis we see that this approach based on neutral zone classifiers presents a useful alternative in classification problems for various applications.