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.
|Published - 2019 Jan 1
|29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
Duration: 2018 Sept 3 → 2018 Sept 6
|29th British Machine Vision Conference, BMVC 2018
|18/9/3 → 18/9/6
Bibliographical noteFunding 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.
© 2018. The copyright of this document resides with its authors.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition