Implementation of EfficientNet-B0 Architecture in Malaria Detection System Based on Patient Red Blood Cell (RBC) Images

Pamungkas, Yuri; Njoto, Edwin Nugroho; Eljatin, Dwinka Syafira; Hardyanti, Intan Fitri; Umamah, Tazkiya; Putri, Kartika Jilan

doi:10.1109/icitri62858.2024.10699220

2024 International Conference on Information Technology Research and Innovation (ICITRI) 2024

DOI: 10.1109/icitri62858.2024.10699220

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Implementation of EfficientNet-B0 Architecture in Malaria Detection System Based on Patient Red Blood Cell (RBC) Images

Yuri Pamungkas,

Edwin Nugroho Njoto,

Dwinka Syafira Eljatin

et al.

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2024

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An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images

Boit,

Patil

2024

Diagnostics

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Background: Malaria is a life-threatening disease spread by infected mosquitoes, affecting both humans and animals. Its symptoms range from mild to severe, including fever, muscle discomfort, coma, and kidney failure. Accurate diagnosis is crucial but challenging, relying on expert technicians to examine blood smears under a microscope. Conventional methods are inefficient, while machine learning approaches struggle with complex tasks and require extensive feature engineering. Deep learning, however, excels in complex tasks and automatic feature extraction. Objective: This paper presents EDRI, which is a novel hybrid deep learning model that integrates multiple architectures for malaria detection from red blood cell images. The EDRI model is designed to capture diverse features and leverage multi-scale analysis. Methods: The proposed EDRI model is trained and evaluated on the NIH Malaria dataset comprising 27,558 labeled microscopic red blood cell images. Results: Experiments demonstrate its effectiveness, achieving an accuracy of 97.68% in detecting malaria, making it a valuable tool for clinicians and public health professionals. Conclusions: The results demonstrate the effectiveness of proposed model’s ability to detect malaria parasite from red blood cell images, offering a robust tool for rapid and reliable malaria diagnosis.

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An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images

Boit,

Patil

2024

Diagnostics

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Implementation of EfficientNet-B0 Architecture in Malaria Detection System Based on Patient Red Blood Cell (RBC) Images

Cited by 1 publication

References 24 publications

An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images

An Efficient Deep Learning Approach for Malaria Parasite Detection in Microscopic Images

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