A lightweight, automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria stage classifier

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths increased between 2019 and 2020 [1]. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. Here, we describe an AI-based approach that boosts the performance of light (LM), atomic force (AFM) and fluorescence microscopy (FM)-based malaria diagnosis. As the main challenge, the stage-specific recognition of infected red blood cells (RBCs) usually requires large sets of microscopy images for training a neural network, which is difficult to obtain. Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition that works even with limited training sets due to a smart reduction of data dimension. Individual RBCs are extracted from an image, reduced to characteristic one-dimensional cross-sections, and classified. We show that our method is applicable to images recorded by various microscopy techniques. It is available as a software package at https://github.com/KatharinaPreissinger/Malaria_stage_classifier and can be used within a python environment. Technical support is provided by the corresponding author (katharina.preissinger@physik.uni-augsburg.de). Author summaryThe Malaria Stage Classifier is a software helping the user to detect and stage RBCs infected with malaria. Accurate recognition of malaria infected RBCs still imposes a challenge in endemic regions, as it is time-consuming and subjective. These deficiencies can be overcome by autonomous computer assisted recognition using neural networks (NNs). The Malaria Stage Classifier offers a user-friendly interface for the stage-specific classification of malaria infected RBCs into four categories--healthy ones and three classes of infected ones according to the parasite age. The use of data reduction, which forms the central element of the Malaria Stage Classifier, allows for a fast and accurate classification of RBCs. It is applicable for light, atomic force, and fluorescence microscopy images and allows for retraining the implemented NN with new images. Our simple concept further has the potential to be generalised for the classification of other cells or objects.