Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3630-3630 | Back to browse issues page

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Haryanto S E V, Abdul Nasir A S, Yusoff Mashor M, Riza B S, Mohamed Z. Deep Learning for Identification Malaria Diseases from Microscopic Image. IJEEE 2025; 21 (2) :3630-3630
URL: http://ijeee.iust.ac.ir/article-1-3630-en.html
Deep Learning for Identification Malaria Diseases from Microscopic Image
Edy Victor Haryanto S , Aimi Salihah Abdul Nasir , Mohd Yusoff Mashor , Bob Subhan Riza , Zeehaida Mohamed
Abstract:   (163 Views)
Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.
Keywords: Malaria diseases, deep learning, microscopic image, identification
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Type of Study: Only For Articles of ELECRiS 2024 | Subject: Biomedical Signal & Image Processing
Received: 2024/12/23 | Revised: 2025/03/14 | Accepted: 2025/02/22
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.