Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre-processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception-ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.

中文翻译：

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使用基于 Inception-ResNet 模型和支持向量机分类器的改进卷积神经网络进行视网膜疾病预测

人工智能和深度学习通过实验帮助眼部疾病的治疗，包括从虹膜、眼底或视网膜图像中自动识别疾病。自动诊断系统（ADS）为人类的利益提供服务，对于早期发现有害疾病至关重要。事实上，早期发现对于避免完全失明至关重要。在现实生活中，会进行一些诊断测试，例如目视眼压计、视网膜检查和敏锐度测试，但它们最终对患者来说既费时又压力大。为了节省时间并更早地发现视网膜疾病，设计了一种有效的预测方法。在这个提出的模型中，第一个过程是数据收集，其中包括用于测试和训练的视网膜疾病数据集。第二个过程是预处理，执行图像调整大小和噪声过滤以进行特征提取。第三步是特征提取，基于Inception-ResNet V2提取图像的形状、大小、颜色和纹理，并用CNN进行分类。分类过程是通过使用 SVM 和提取的特征来完成的。疾病的预测分为正常、白内障、青光眼、视网膜疾病等。使用准确度、误差、灵敏度、精确度等性能指标来评估建议模型的性能。建议模型的准确度、误差、灵敏度和精度分别为 0.96、0.962、0.964 和 0.04，高于 VGG16、Mobilenet V1、ResNet 和 AlexNet 等现有技术。因此，所提出的模型可以立即预测视网膜疾病。