AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images
AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images
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DOI：
10.1007/s11517-019-02066-y
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发表时间：
2019-11-28
影响因子：
3.2
通讯作者：
Alqudah, Ali Mohammad
中科院分区：
文献类型：
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作者：
Alqudah, Ali Mohammad
自引入光学相干断层扫描（OCT）技术进行二维眼成像以来，它已成为视网膜眼病无创评估中最重要和应用最广泛的成像方式之一。年龄相关性黄斑变性（AMD）和糖尿病性黄斑水肿眼病是使用OCT诊断失明的主要原因。最近，随着机器学习和深度学习技术的发展，利用OCT图像对眼部视网膜疾病进行分类已经成为一个相当大的挑战。本文提出了一种基于光谱域光学相干层析成像（SD-OCT）的多类分类系统的自动卷积神经网络（CNN）结构。该系统用于分类五种类型的视网膜疾病（年龄相关性黄斑变性（AMD），脉络膜新生血管（CNV），糖尿病性黄斑水肿（DME）和drusen）除了正常情况下。本文提出的带有softmax分类器的CNN架构总体上正确识别了100%的AMD病例、98.86%的CNV病例、99.17%的DME病例、98.97%的dren病例和99.15%的normal病例，总体准确率为95.30%。该架构是使用SD-OCT图像诊断视网膜疾病的潜在影响工具。
Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging modalities for the noninvasive assessment of retinal eye diseases. Age-related macular degeneration (AMD) and diabetic macular edema eye disease are the leading causes of blindness being diagnosed using OCT. Recently, by developing machine learning and deep learning techniques, the classification of eye retina diseases using OCT images has become quite a challenge. In this paper, a novel automated convolutional neural network (CNN) architecture for a multiclass classification system based on spectral-domain optical coherence tomography (SD-OCT) has been proposed. The system used to classify five types of retinal diseases (age-related macular degeneration (AMD), choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen) in addition to normal cases. The proposed CNN architecture with a softmax classifier overall correctly identified 100% of cases with AMD, 98.86% of cases with CNV, 99.17% cases with DME, 98.97% cases with drusen, and 99.15% cases of normal with an overall accuracy of 95.30%. This architecture is a potentially impactful tool for the diagnosis of retinal diseases using SD-OCT images.