Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia

Research Article

Lymphatic Filariasis Detection Using Image Analysis

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  • @INPROCEEDINGS{10.4108/eai.11-10-2022.2325548,
        author={Ayu  Elvana and Eka Dodi  Suryanto},
        title={Lymphatic Filariasis Detection Using Image Analysis},
        proceedings={Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia},
        publisher={EAI},
        proceedings_a={ICIESC},
        year={2022},
        month={12},
        keywords={lymphatic filariasis image processing cnn},
        doi={10.4108/eai.11-10-2022.2325548}
    }
    
  • Ayu Elvana
    Eka Dodi Suryanto
    Year: 2022
    Lymphatic Filariasis Detection Using Image Analysis
    ICIESC
    EAI
    DOI: 10.4108/eai.11-10-2022.2325548
Ayu Elvana1,*, Eka Dodi Suryanto1
  • 1: Universitas Negeri Medan
*Contact email: ayuelvana@unimed.ac.id

Abstract

Elephantiasis is generally detected through microscopic examination of blood. Until now, this has been difficult because microfilariae only appear in the blood at night for a few hours (nocturnal periodicity). The lack of trained microscopy technicians is a serious problem. Due to the repetitive and tedious nature of diagnosis and the fact that there are few positive cases in a population of thousands. This is a contributing factor to increased detection errors. The main problem encountered is the high degree of difficulty and precision and the long time it takes to perform laboratory examinations. Image analysis method can be used as a way to identify Lymphatic Filariasis worms in the blood. Based on the description above, it can be said that the detection of Lymphatic Filariasis worms can be done with digital image analysis. This research will use the feature extraction method and Convolutional Neural Network to identify object features in the form of worms that cause elephantiasis (Lymphatic Filariasis) in digital images recorded by Trinocular digital microscope cameras. This study aims to determine the performance of image analysis methods used in the identification process of Lymphatic Filariasis worms using digital images recorded by a Compound Trinocular microscope