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Iranian Journal of Electrical and Electronic Engineering، جلد ۲۱، شماره ۲، صفحات ۳۶۳۰-۳۶۳۰
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عنوان انگلیسی Deep Learning for Identification Malaria Diseases from Microscopic Image
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Malaria diseases, deep learning, microscopic image, identification
نویسندگان مقاله | Edy Victor Haryanto S
Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia.


| Aimi Salihah Abdul Nasir
Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia.


| Mohd Yusoff Mashor
Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia.


| Bob Subhan Riza
Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia.


| Zeehaida Mohamed
Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Malaysia.
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5468-2&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده 4-Biomedical Signal & Image Processing
نوع مقاله منتشر شده Only For Articles of ELECRiS 2024
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