Abstract
Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0:948, 0:886, and 0:875, respectively, which competes with state-of-the-art approaches.
Original language | English (US) |
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DOIs | |
State | Published - 2021 |
Event | 17th International Symposium on Medical Information Processing and Analysis - Campinas, Brazil Duration: Nov 17 2021 → Nov 19 2021 |
Conference
Conference | 17th International Symposium on Medical Information Processing and Analysis |
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Country/Territory | Brazil |
City | Campinas |
Period | 11/17/21 → 11/19/21 |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering