A lightweight, automated neural network-based stage-specific malaria detection software using dimension reduction
Katharina Preißinger, I. Kézsmárki, János Török · Zenodo (CERN European Organization for Nuclear Research) · 2022
Artificial intelligence (AI) can outperform human in various applications, including diagnostics. Due to climate change and the COVID-19 pandemic, the number of malaria infections is rising. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which AI has recently been demonstrated to have a great potential. Here we describe an AI-based approach that boosts the performance of light, atomic force and fluorescence microscopy-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 which 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. The method is applicable to images recorded by various microscopy techniques. It is housed within a GitHub repository at https://github.com/KatharinaPreissinger/Malaria_stage_classifier.