Optical coherence tomography image for automatic classification of diabetic macular edema

被引：0

作者：

WANG P. ^{[1
]}

LI J.-L. ^{[1
]}

DING H. ^{[1
]}

机构：

[1] Department of Electronics Information Engineering, School of Information Engineering, Nanchang University, Nanchang

来源：

Optica Applicata | 2021年 / 50卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Diabetic macular edema; Fine-tuning; Optical coherence tomography; Transfer learning;

D O I：

10.37190/OA200405

中图分类号：

学科分类号：

摘要：

Diabetic macular edema (DME) is the dominant reason of diabetic visual loss, so early detection and treatment of DME is of great significance for the treatment of diabetes. Based on transfer learning, an automatic classification method is proposed to distinguish DME images from normal images in optical coherence tomography (OCT) retinal fundus images. Features of the DME are automatically identified and extracted by the pre-trained convolutional neural network (CNN), which only involves fine-tuning the VGGNet-16 network without any user intervention. An accuracy of 97.9% and a sensitivity of 98.0% are acquired with the OCT images in the Duke data set from experimental results. The proposed method, a core part of an automated diagnosis system of the DME, revealed the ability of fine-tuning models to train non-medical images, allowing them can be classified with limited training data. Moreover, it can be developed to assist early diagnosis of the disease, effectively delaying (or avoiding) the progression of the disease, consequently. © 2020 WrocÅ‚aw University of Science and Technology. All rights reserved.

引用

页码：567 / 577

页数：10

共 28 条

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