Abstract：
Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular retinal diseases, playing a crucial role in diagnosing retinopathy while maintaining a non-invasive modality. The increasing volume of OCT images underscores the growing importance of automating image analysis. Age-related diabetic Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the most common cause of visual impairment. Early detection and timely intervention for diabetes-related conditions are essential for preventing optical complications and reducing the risk of blindness. This study introduces a novel Computer-Aided Diagnosis (CAD) system based on a Convolutional Neural Network (CNN) model, aiming to identify and classify OCT retinal images into AMD, DME, and Normal classes. Leveraging CNN efficiency, including feature learning and classification, various CNN, including pre-trained VGG16, VGG19, Inception_V3, a custom from scratch model, BCNN (VGG16) 2 , BCNN (VGG19) 2 , and BCNN (Inception_V3) 2 , are developed for the classification of AMD, DME, and Normal OCT images. The proposed approach has been evaluated on two datasets, including a DUKE public dataset and a Tunisian private dataset. The combination of the Inception_V3 model and the extracted feature from the proposed custom CNN achieved the highest accuracy value of 99.53% in the DUKE dataset. The obtained results on DUKE public and Tunisian datasets demonstrate the proposed approach as a significant tool for efficient and automatic retinal OCT image classification.
摘要：
光学相干断层扫描（OCT）被广泛认为是评估眼视网膜疾病的主要方法。在诊断视网膜病变中发挥关键作用，同时保持非侵入性模式。OCT图像体积的增加强调了自动化图像分析的日益重要。年龄相关性糖尿病性黄斑变性(AMD)和糖尿病性黄斑水肿(DME)是视觉障碍的最常见原因。早期发现和及时干预糖尿病相关疾病对于预防光学并发症和降低失明风险至关重要。本研究介绍了一种基于卷积神经网络（CNN）模型的新型计算机辅助诊断（CAD）系统，旨在将OCT视网膜图像识别和分类为AMD，DME,和普通班。利用CNN的效率，包括特征学习和分类，各种CNN,包括预先训练的VGG16，VGG19，Inception_V3，一个自定义的从头开始模型，BCNN(VGG16)2,BCNN(VGG19)2,和BCNN(Inception_V3)2，是为AMD的分类而开发的，DME,和正常OCT图像。所提出的方法已经在两个数据集上进行了评估，包括DUKE公共数据集和突尼斯私人数据集。Inception_V3模型和从所提出的自定义CNN中提取的特征的组合在DUKE数据集中实现了99.53％的最高精度值。在DUKE公共和突尼斯数据集上获得的结果表明，所提出的方法是有效和自动视网膜OCT图像分类的重要工具。
