Abstract
Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of ρ = 0.998 for parasites and ρ = 0.987 for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose.
Highlights
Introduction nal affiliationsMalaria is still a global health concern with nearly half of the world’s population at risk
We propose the use of novel pre-trained deep learning models for a multi-class detection task and Mobile smart phone app for automated localization and quantification of malaria density in thick blood smears
We show that Faster R-Convolutional Neural Network (CNN) ResNet 101 model produces better detection accuracy for our proposed multi-class detection task for trophozoites (F1 score = 0.7897) and White Blood Cells (WBCs) (F1 score = 0.8426)
Summary
The authors use a combination of color and texture features to characterize segmented RBCs and a linear Support Vector Machine (SVM) model to classify infected and uninfected cells These studies have mainly focused on thin blood smear images [15]. Yang et al [5] developed a smartphone application based on intensity-based Iterative Global Minimum Screening (IGMS) The researchers use this method for automatic pre-selection of malaria parasites in thick blood smears and a customized Convolutional Neural Network (CNN) classification model of the parasite. As opposed to semi-automated and customized CNN models for classification of tasks on malaria parasitemia, this study presents an end-to-end automated pre-trained deep learning approach for detection and counting of malaria parasites and WBCs for improved parasitemia determination in thick blood smears. This is well suited for improving malaria parasitemia quantification in resource-constrained settings with few skilled lab technologists to interpret microscopy test results, as well as facilitating training of the models in a low data regime
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