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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Hoque, Md. Jiabul
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells Hoque, Md. Jiabul; Islam, Md. Saiful; Khaliluzzaman, Md.; Muntasir, Abdullah Al; Mohsin, Mohammad Abdullah Bin
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4518-4530

Abstract

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection.
Co-Authors Al Noman, Abdullah Chowdhury, Irfan A. Hoque, Md. Abrarul Khaliluzzaman, Md. Md. Saiful Islam Mohsin, Mohammad Abdullah Bin Muntasir, Abdullah Al Saifuddin, Mohammad