Deep Learning Approach for Malaria Parasite Detection in Thick Blood Smear Images

Nugroho, Hanung Adi and Nurfauzi, Rizki (2021) Deep Learning Approach for Malaria Parasite Detection in Thick Blood Smear Images. In: 2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering, 13-15 October 2021, Depok, Indonesia.

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Abstract

Malaria is caused by a bite of female anopheles mosquitos transmitting the parasite Plasmodium into human bodies. Malaria is a common disease in tropical and subtropical regions and is also a severe public health problem due to its risk. Early diagnosis is required to avoid the hazard of death from malaria. Microscopic analysis of blood smears remains a standard method for malaria analysis. However, manual microscopic observation is laborious, and the results have a heavy dependence on the examiner's skill. To alleviate this problem, this study proposed a deep learning method for detecting malaria automatically malaria parasite on thick blood smear microscopic images. The proposed approach achieved the fastest examination at 0.25 sec/image or more than 20 times faster compared to that of previous with mAP, sensitivity, and a precision score of 72, 78.4, and 83.2 , respectively. These performances indicated that the proposed approach can be a promising alternative to CAD systems for fast parasite detection. ©2021 IEEE

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 6
Uncontrolled Keywords: Blood; Deep learning; Diagnosis; Health risks; Tropics; Anopheles mosquitoes; Blood smears; Deep learning; Detection; Fast-RCNN; Learning approach; Malaria parasite; Microscopic image; Thick; Diseases
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Electronics Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 28 Oct 2024 04:24
Last Modified: 28 Oct 2024 04:24
URI: https://ir.lib.ugm.ac.id/id/eprint/8544

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