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Open AccessArticle

Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears

1
Department of Computer Science, College of Computing and Infromation Sciences, Makerere University, Kampala P.O. Box 7062, Uganda
2
Sunbird AI, Kampala P.O. Box 11296, Uganda
3
Department of Information Technology, College of Computing and Infromation Sciences, Makerere University, Kampala P.O. Box 7062, Uganda
*
Author to whom correspondence should be addressed.
Received: 1 December 2020 / Revised: 2 January 2021 / Accepted: 5 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application)
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. View Full-Text
Keywords: deep learning; thick blood smear; parasite count; white blood cell count; parasitemia determination; Faster R-CNN; SSD MobileNet; smartphone deployment deep learning; thick blood smear; parasite count; white blood cell count; parasitemia determination; Faster R-CNN; SSD MobileNet; smartphone deployment
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MDPI and ACS Style

Nakasi, R.; Mwebaze, E.; Zawedde, A. Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears. Algorithms 2021, 14, 17. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010017

AMA Style

Nakasi R, Mwebaze E, Zawedde A. Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears. Algorithms. 2021; 14(1):17. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010017

Chicago/Turabian Style

Nakasi, Rose; Mwebaze, Ernest; Zawedde, Aminah. 2021. "Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears" Algorithms 14, no. 1: 17. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010017

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