An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network

Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation in original U-net model was replaced by the convo...

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Bibliographic Details
Published in:Journal of medical systems Vol. 43; no. 9; pp. 304 - 9
Main Authors: Li, Qianjin, Fan, Shanshan, Chen, Changsheng
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2019
Springer Nature B.V
Subjects:
Algorithms
Convolution
Deep learning
Diabetes
Diabetes mellitus
Diabetic retinopathy
Diabetic Retinopathy - diagnosis
Diagnosis
Distributed Analytics and Deep Learning in Health Care
Feature extraction
Health Informatics
Health Sciences
Humans
Image & Signal Processing
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Medical diagnosis
Medicine
Medicine & Public Health
Neural Networks, Computer
Retina - diagnostic imaging
Retinopathy
Sensitivity and Specificity
Statistics for Life Sciences
Training
Deep learning
Generalization performance
Diabetic retinopathy
Fully convolutional network
U-net model
Intelligent diagnosis
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary:Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based image automatic segmentation and diagnosis algorithm was proposed, in which the max-pooling operation in original U-net model was replaced by the convolution operation to keep more feature information. Firstly, the regions of 128×128 were extracted from all slices of the patients as data samples. Secondly, the patient samples were divided into training sample set and testing sample set, and data augmentation was performed on the training samples. Finally, all the training samples were adopted to train the model. Compared with Fully Convolutional Network (FCN) model and max-pooling based U-net model, DSC and CR coefficients of the proposed method achieve the best results, while PM coefficient is 2.55 percentage lower than the maximum value in the two comparison models, and Average Symmetric Surface Distance is slightly higher than the minimum value of the two comparison models by 0.004. The experimental results show that the proposed model can achieve good segmentation and diagnosis results.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1432-0