Abstract：
The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.
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摘要：
视力障碍的患病率正以惊人的速度增长。该研究的目标是创建一种自动化方法，该方法使用光学相干断层扫描（OCT）将视网膜疾病分为四类：脉络膜新生血管，糖尿病性黄斑水肿,玻璃疣,和正常病例。这项研究提出了一个新的框架，结合了机器学习和基于深度学习的技术。使用的分类器是支持向量机(SVM)，K-近邻(K-NN),决策树(DT)，和集成模型(EM)。特征提取器，InceptionV3卷积神经网络，也被雇用了。使用18000张OCT图像的数据集针对9个标准评估模型的性能。对于SVM，K-NN,DT,和EM分类器，分析显示了最先进的表现，分类准确率为99.43%，99.54%,97.98%,99.31%,分别。已经引入了一种有前途的方法来自动识别和分类视网膜疾病，减少人为错误，节省时间。
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