Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

ABSTRACT: Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories

SUBMITTER: Choi JY 

PROVIDER: S-EPMC5667846 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature