Malaria detection using machine learning

Asma Almakhzoumi; Talal Bonny; Mohammad Al-Shabi

doi:10.1117/12.3014636

7 April 2024 Malaria detection using machine learning

Asma Almakhzoumi, Talal Bonny, Mohammad Al-Shabi

Proceedings Volume 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII; 1299813 (2024) https://doi.org/10.1117/12.3014636
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

Malaria, a significant global health concern, necessitates precise diagnostic tools for effective management. This study introduces an innovative approach to malaria detection using advanced machine-learning techniques. By harnessing convolutional neural networks (CNNs) and deep learning, the research presents a robust framework for automated malaria detection through microscopic images of red blood cells. The study evaluates three key algorithms—CNN, VGG-16, and Support Vector Machine (SVM)—using a meticulously curated dataset of 27,560 images. Results highlight the VGG-16 model’s exceptional accuracy, achieving 98.5%. Transfer learning is pivotal in its success, demonstrating the power of pre-trained models for medical image analysis. This research advances automated disease diagnosis, particularly in resource-limited settings. Future work involves fine-tuning algorithms, exploring ensemble techniques, and enhancing interpretability for broader healthcare applications.

Citation Download Citation

Asma Almakhzoumi, Talal Bonny, and Mohammad Al-Shabi "Malaria detection using machine learning", Proc. SPIE 12998, Optics, Photonics, and Digital Technologies for Imaging Applications VIII, 1299813 (7 April 2024); https://doi.org/10.1117/12.3014636

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