A Trilingual Android Application with Automatic Malaria Detection from Microscopic Images of Red Blood Cells
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Date
2023-12
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Addis Ababa University
Abstract
Malaria, which is a mosquito-borne blood disease caused by Plasmodium parasites, is one of the virulent
infectious diseases affecting human beings and other animals since antiquity. Even though there
were promising progresses in the reduction of malaria morbidity and mortality in the past two decades
before the outbreak of COVID-19, the latest two reports of theWorld Health Organization (WHO) statistics
indicate that malaria has been overlooked due to the COVID pandemics. Malaria is still prevalent
specifically in low resource setting areas such as the sub-Saharan African countries, including Ethiopia.
WHO reported that there were 229 million new cases of malaria and 409,000 deaths globally in 2019,
alone. Whereas in the year 2021, the morbidity and mortality was reported to rise up to 247 million
and 619,000, respectively. Timely diagnosis and treatment as well as good awareness about the disease
play a major role to combat malaria. In the current project work, it was intended to design and develop
a multi-lingual Android App that offers useful information about the malaria disease and is capable of
automatically detecting malaria infected red blood cells (RBCs) from color microscopic images based
on a deep learning approach. The Convolutional Neural Network (CNN) based deep learning model
was trained, validated and tested on a publicly available dataset composed of microscopic images of
RBCs taken from individuals with confirmed malaria infection as well as normal control groups. Experimental
results generated from the deep learning model showed that the detection capability of the
model achieved 100% training accuracy, 96% validation accuracy and 96% testing accuracy. The developed
App avails useful information about malaria disease in general and tips users with fundamental
information regarding its prevention and transmission mechanisms acting as an m-health system.
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Keywords
Malaria, Android App, Deep Learning, CNN, Automation