2018/07/24 by Maria Camila Alvarez Trivino, Jérémie Despraz, Trivino, Maria Camila Alvarez +5
Health Professions · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Retinal Imaging and Analysis
paper · pdf · doi:10.48550/arxiv.1807.09232
openalex publication_date 2018/07/24 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
In this project, we developed a deep learning system applied to human retina images for medical diagnostic decision support. The retina images were provided by EyePACS. These images were used in the framework of a Kaggle contest, whose purpose to identify diabetic retinopathy signs through an automatic detection system. Using as inspiration one of the solutions proposed in the contest, we implemented a model that successfully detects diabetic retinopathy from retina images. After a carefully designed preprocessing, the images were used as input to a deep convolutional neural network (CNN). The CNN performed a feature extraction process followed by a classification stage, which allowed the system to differentiate between healthy and ill patients using five categories. Our model was able to identify diabetic retinopathy in the patients with an agreement rate of 76.73% with respect to the medical expert's labels for the test data.