Publication Date:
2018-06-21
Description:
Algorithms, Vol. 11, Pages 88: Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images Algorithms doi: 10.3390/a11060088 Authors: Qingge Ji Wenjie He Jie Huang Yankui Sun We present an automatic method based on transfer learning for the identification of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retinal optical coherence tomography (OCT) images. The algorithm aims to improve the classification performance of retinal OCT images and shorten the training time. Firstly, we remove the last several layers from the pre-trained Inception V3 model and regard the remaining part as a fixed feature extractor. Then, the features are used as input of a convolutional neural network (CNN) designed to learn the feature space shifts. The experimental results on two different retinal OCT images datasets demonstrate the effectiveness of the proposed method.
Electronic ISSN:
1999-4893
Topics:
Computer Science
