Malaria Parasite Detection from RBCs Images Using Deep Learning Techniques

Gais Alhadi Babikir¹ , Awadallah M. Ahmed² , Lamyaa Alser Mohammed³

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.5 , pp.75-81, Oct-2023

Online published on Oct 31, 2023

Copyright © Gais Alhadi Babikir, Awadallah M. Ahmed, Lamyaa Alser Mohammed . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Malaria is one the life-threaten diseases spread in many countries worldwide with high infection rates in tropical and subtropical countries. According to WHO half of the world`s population is at risk of malaria, and nearly every minute malaria kills a child in the world. Fortunately, this disease is preventable and treatable but accurate and fast diagnosis is a crucial stage of malaria treatment. Many methods have been used in malaria detection ranging from traditional that are based on human experts and microscopes to machine-based methods that depend on machine learning and deep learning. This paper proposes a deep-learning method for malaria detection from RBCs images. Obviously, three transfer learning models (MobileNet, Xception, and InceptionV3) were proposed and compared based on their precision, recall, f1-Score, and accuracy. Hence, by comparing the three models, the MobileNet model is the best in terms of overall accuracy (99.04%), we can confirm that with the results of area under the curve (0.981). Therefore, the pre-trained MobileNet model can effectively contribute to malaria classification from RBCs images.

Key-Words / Index Term :
Disease Detection, Malaria, CNN, Deep Learning Techniques, MobileNet, Xception, InceptionV3

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