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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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) |
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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 > Electrical and Information Technology 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 |