phat 22(5): e2

Research Article

Automated Detection of fundus retinal image using EGODD algorithm

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  • @ARTICLE{10.4108/eetpht.v8i5.3170,
        author={Subha K. and S. Kother Mohideen},
        title={Automated Detection of fundus retinal image using EGODD algorithm},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={8},
        number={5},
        publisher={EAI},
        journal_a={PHAT},
        year={2022},
        month={11},
        keywords={Optic Disc Detection, Eye Fundus image, machine learning techniques},
        doi={10.4108/eetpht.v8i5.3170}
    }
    
  • Subha K.
    S. Kother Mohideen
    Year: 2022
    Automated Detection of fundus retinal image using EGODD algorithm
    PHAT
    EAI
    DOI: 10.4108/eetpht.v8i5.3170
Subha K.1,*, S. Kother Mohideen1
  • 1: Sri Ram Nallamani Yadava College of Arts and Science, Tirunelveli, Tamil Nadu, India
*Contact email: subhakandhi@gmail.com

Abstract

The developed device is tested for optical disc identification in the retinal image of the fundus. The theme chosen was interesting, as optical disc detection plays a critical role in retinal image processing for recognizing other fundus structures and is essential for identifying eye-related diseases. Because human visual vision has been little researched for optical disc detection and an effort to extract data analytics based on eye eyes, the proposed study first discovers how human perception functions for optical disc detection using a bottom-up visual focus paradigm. Eye-view data as the user conducts basic target-search tasks are obtained from separate user groups made up of experts and no expert groups. Extensive data processing has been carried out to extract eye gazing characteristics such as fixation and using computer approach mark regions in the fundus' retinal picture. Segregated labeled data was used to build up a piece of top-down information to bend the search map to the target area. The resulting Eye-based Optical Disk Detection System (EGODD) was tested through normal machine learning algorithms. This proposed framework has been tested and approved for optical disc detection; however, it can also be applied to other applications.