Towards life cycle identification of malaria parasites using machine learning and Riemannian geometry

TL;DR

This paper presents a cost-effective malaria diagnostic system using machine learning and Riemannian geometry, automating parasite detection and life-stage identification to improve accuracy and speed over manual methods.

Contribution

It introduces a novel hardware-software system with a patch-based clustering algorithm and manifold optimization for robust, high-dimensional blood image analysis.

Findings

Higher speed than supervised systems

Accuracy comparable or better than existing methods

Effective across various imaging conditions

Abstract

Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant diagnostic method for malaria. However, it is time consuming and prone to human errors. The aim of this paper is to automate the diagnosis process and minimize the human intervention. We have developed the hardware and software for a cost-efficient malaria diagnostic system. This paper describes the manufactured hardware and also proposes novel software to handle parasite detection and life-stage identification. A motorized microscope is developed to take images from Giemsa-stained blood smears. A patch-based unsupervised statistical clustering algorithm is proposed which offers a novel method for classification of different regions within blood images. The proposed method provides better robustness against different imaging settings. The core of the proposed algorithm is a model called Mixture of Independent Component Analysis. A manifold based optimization method is proposed that facilitates the application of the model for high dimensional data usually acquired in medical microscopy. The method was tested on 600 blood slides with various imaging conditions. The speed of the method is higher than current supervised systems while its accuracy is comparable to or better than them.