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All Journal Journal of Electronics Technology Exploration (JOeTEx)
Yana, Natasya Siska Fitri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : Journal of Electronics Technology Exploration (JOeTEx)

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Malaria Disease Detection System in Humans Using Convolutional Neural Network (CNN) Yana, Natasya Siska Fitri; Shabaha, Achmad Rozin; Unjung, Jumanto
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.646

Abstract

Malaria is a deadly disease transmitted by the Plasmodium parasite. Detection is performed by trained microscopists who analyze microscopic images of blood smears. This analysis can be done automatically using modern deep learning techniques. The need for skilled labor can be significantly reduced by developing accurate and efficient automated models. In this article, we propose a fully automated convolutional neural network (CNN)-based model for diagnosing malaria from microscopic images of blood smears. Various techniques including knowledge distillation, data augmentation, autoencoder, feature extraction with CNN model to optimize and improve model accuracy and reasoning performance. Our deep learning model can detect malaria parasites from microscopic images with 95% accuracy requiring more than 27,600 images. This shows that the mode is able to provide more accurate predictions compared to malaria disease detection models using other algorithms such as in previous studies with an accuracy of 90%. By using CNN algorithm, this article can contribute novelty in the development of effective malaria detection methods for malaria disease.
Co-Authors Jumanto Unjung Shabaha, Achmad Rozin