An automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria microscopy classifier

Preißinger Katharina; Kézsmárki István; Török János

doi:10.1016/j.mex.2023.102189

An automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria microscopy classifier

MethodsX. 2023 Apr 20:10:102189. doi: 10.1016/j.mex.2023.102189. eCollection 2023.

Authors

Preißinger Katharina^{1

2

3

4}, Kézsmárki István^{3

4}, Török János^{5

6}

Affiliations

¹ Department of Applied Biotechnology and Food Sciences, BME, Budapest 1111, Hungary.
² Research Center for Natural Sciences, Institute of Enzymology, Budapest 1111, Hungary.
³ Department of Physics, BME, Budapest 1111, Hungary.
⁴ Department of Experimental Physics V, University of Augsburg, Augsburg 86159, Germany.
⁵ Department of Theoretical Physics, Institute of Physics, BME, Műegyetem rkp. 3, Budapest H-1111, Hungary.
⁶ MTA-BME Morphodynamics Research Group, BME, Budapest 1111, Hungary.

Abstract

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths, caused by the Plasmodium genus, of which P. falciparum is the most common and lethal to humans, increased between 2019 and 2020. 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. One of the main reasons for the use of neural networks (NNs) is the time saving through automatising the process and the elimination of human error. When classifying with two-dimensional images of red blood cells (RBCs), the number of parameters fitted by the NN for the classification of RBCs is extremely high, which strongly influences the performance of the network, especially for training sets of moderate size. The complicated handling of malaria culturing and sample preparation does not only limit the efficiency of NNs due to small training sets, but also because of the uneven distribution of red blood cell (RBC) categories. To boost the performance of microscopy techniques in malaria diagnosis, our approach aims at resolving these drawbacks by reducing the dimension of the input data and by data augmentation, respectively. We assess the performance of our approach on images recorded by light (LM), atomic force (AFM), and fluorescence microscopy (FM). Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition by (1) identifying individual RBCs in multi-cell microscopy images, (2) extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to (3) classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package.•Identifying individual RBCs in multi-cell microscopy images.•Extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to.•Classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package.

Keywords: Artificial intelligence; Cell detection; Malaria; Malaria diagnosis; Red blood cells; Software; The malaria stage classifier.