2Sranking-cnn: A 2-stage ranking-CNN for diagnosis of glaucoma from fundus images using CAM-extracted ROI as an intermediate input

Tae Joon Jun, Dohyeun Kim, Hoang Minh Nguyen, Daeyoung Kim, Youngsub Eom

    Research output: Contribution to conferencePaperpeer-review

    2 Citations (Scopus)


    Glaucoma is a disease in which the optic nerve is chronically damaged by the elevation of the intra-ocular pressure, resulting in visual field defect. Therefore, it is important to monitor and treat suspected patients before they are confirmed with glaucoma. In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma. Furthermore, we propose a method of using the class activation map as a mask filter and combining it with the original fundus image as an intermediate input. Our results have improved the average accuracy by about 10% over the existing 3-class CNN and ranking-CNN, and especially improved the sensitivity of suspicious class by more than 20% over 3-class CNN. In addition, the extracted ROI was also found to overlap with the diagnostic criteria of the physician. The method we propose is expected to be efficiently applied to any medical data where there is a suspicious condition between normal and disease.

    Original languageEnglish
    Publication statusPublished - 2019 Jan 1
    Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
    Duration: 2018 Sept 32018 Sept 6


    Conference29th British Machine Vision Conference, BMVC 2018
    Country/TerritoryUnited Kingdom

    Bibliographical note

    Funding Information:
    This research was supported by International Research & Development Program of the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT&Future Planning of Korea (2016K1A3A7A03952054) and support of Korea University Ansan Hospital providing fundus images and clinical advices for this research are gratefully acknowledged.

    Publisher Copyright:
    © 2018. The copyright of this document resides with its authors.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition


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