Deep Learning for Identification Malaria Diseases from Microscopic Image
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-con...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-06-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3630-en.pdf |
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| author | Edy Victor Haryanto S Aimi Salihah Abdul Nasir Mohd Yusoff Mashor Bob Subhan Riza Zeehaida Mohamed |
| author_facet | Edy Victor Haryanto S Aimi Salihah Abdul Nasir Mohd Yusoff Mashor Bob Subhan Riza Zeehaida Mohamed |
| author_sort | Edy Victor Haryanto S |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a3f13ccf36ff4778a385eb6a11d53d6b |
| institution | OA Journals |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-a3f13ccf36ff4778a385eb6a11d53d6b2025-08-20T02:07:06ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-06-0121236303630Deep Learning for Identification Malaria Diseases from Microscopic ImageEdy Victor Haryanto S0Aimi Salihah Abdul Nasir1Mohd Yusoff Mashor2Bob Subhan Riza3Zeehaida Mohamed4 Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Malaysia. 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.http://ijeee.iust.ac.ir/article-1-3630-en.pdfmalaria diseasesdeep learningmicroscopic imageidentification |
| spellingShingle | Edy Victor Haryanto S Aimi Salihah Abdul Nasir Mohd Yusoff Mashor Bob Subhan Riza Zeehaida Mohamed Deep Learning for Identification Malaria Diseases from Microscopic Image Iranian Journal of Electrical and Electronic Engineering malaria diseases deep learning microscopic image identification |
| title | Deep Learning for Identification Malaria Diseases from Microscopic Image |
| title_full | Deep Learning for Identification Malaria Diseases from Microscopic Image |
| title_fullStr | Deep Learning for Identification Malaria Diseases from Microscopic Image |
| title_full_unstemmed | Deep Learning for Identification Malaria Diseases from Microscopic Image |
| title_short | Deep Learning for Identification Malaria Diseases from Microscopic Image |
| title_sort | deep learning for identification malaria diseases from microscopic image |
| topic | malaria diseases deep learning microscopic image identification |
| url | http://ijeee.iust.ac.ir/article-1-3630-en.pdf |
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