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Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images

by 1,2, 1,2, 1,2 and 2,3,*
1
School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
2
Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China
3
Department of Computer Science and Technology, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Algorithms 2018, 11(6), 88; https://doi.org/10.3390/a11060088
Received: 4 May 2018 / Revised: 14 June 2018 / Accepted: 17 June 2018 / Published: 20 June 2018
(This article belongs to the Special Issue Machine Learning for Medical Image Analysis)
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. View Full-Text
Keywords: optical coherence tomography; image analysis; age-related macular degeneration; diabetic macular edema; convolutional neural network optical coherence tomography; image analysis; age-related macular degeneration; diabetic macular edema; convolutional neural network
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Ji, Q.; He, W.; Huang, J.; Sun, Y. Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images. Algorithms 2018, 11, 88.

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