The retina is an essential component of the visual system, and maintaining eyesight depends on the timely and correct detection of disorders. This research specifically addresses the early-stage detection and severity classification of diabetic retinopathy (DR), a serious public health hazard. We compare the results of different deep learning models such as InceptionV3, DenseNet121 and other CNN based models by using different image filters, such as Gaussian, grayscale and Gabor. These models could detect subtle pathological alterations and use that information to estimate the risk of retinal illnesses. The objective is to improve the diagnostic processes for diabetic retinopathy, the primary cause of diabetes-related blindness, by utilizing deep learning models. A comparative analysis between Greyscale, Gaussian and Gabor filters has been provided after applying these filters on the retinal images. The Gaussian filter resulted to be the most promising filter giving the best accuracies for all the models. The best performing model was InceptionV3 which gave an accuracy of 96% on Gaussian images, therefore Gaussian filter emerged as our most promising filter.

翻译：视网膜是视觉系统的重要组成部分，及时准确地检测病变对维持视力至关重要。本研究专门针对糖尿病视网膜病变（DR）这一严重的公共卫生威胁，探讨其早期检测与严重程度分类方法。通过使用高斯滤波、灰度滤波和Gabor滤波等不同图像滤波器，我们对比了InceptionV3、DenseNet121及其他基于CNN的深度学习模型的表现。这些模型能够检测细微的病理改变，并利用这些信息评估视网膜疾病风险。本研究的目的是利用深度学习模型改进糖尿病视网膜病变（糖尿病致盲的主要原因）的诊断流程。在将上述滤波器应用于视网膜图像后，我们对灰度滤波、高斯滤波和Gabor滤波进行了比较分析。结果表明，高斯滤波是最具前景的滤波器，所有模型在该滤波下均获得最高准确率。表现最佳的模型为InceptionV3，在高斯滤波图像上取得了96%的准确率，因此高斯滤波被确认为本研究中效果最优的滤波器。