Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

中文翻译：

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使用基于深度学习的 CenterNet 模型从视网膜图像检测糖尿病眼病

糖尿病视网膜病变 (DR) 是一种眼部疾病，它会改变患有糖尿病的人的血管。当 DR 影响黄斑时会发生糖尿病黄斑水肿 (DME)，这会导致黄斑内积液。高效的筛查系统需要专家手动分析图像以识别疾病。然而，由于筛查方法的挑战性和缺乏训练有素的人力资源，设计有效的以筛查为导向的治疗是一项昂贵的任务。自动化系统正在努力应对这些挑战；然而，这些方法不能很好地推广到多种疾病和现实世界的场景。为了解决上述问题，我们提出了一种包括两个主要步骤的新方法。第一个涉及数据集准备和特征提取，另一个涉及改进针对眼病分类训练的基于自定义深度学习的 CenterNet 模型。最初，我们为可疑样本生成注释以定位精确的感兴趣区域，而所提出的解决方案的另一部分在带注释的图像上训练中心网络模型。具体来说，我们使用 DenseNet-100 作为特征提取方法，在该方法上使用单级检测器 CenterNet 对疾病病变进行定位和分类。我们在具有挑战性的数据集 APTOS-2019 和 IDRiD 上评估了我们的方法，平均准确率分别为 97.93% 和 98.10%。我们还使用基准 EYEPACS 和 Diaretdb1 数据集进行了跨数据集验证。定性和定量结果都表明，由于 CenterNet 更有效的定位能力，我们提出的方法优于最先进的方法，因为它可以轻松识别小病变并处理过度拟合的训练数据。我们提出的框架擅长正确定位和分类疾病病变。与现有的 DR 和 DME 分类方法相比，我们的方法可以从低强度和嘈杂的图像中提取具有代表性的关键点并对其进行准确分类。因此，我们的方法可以在 DR 和 DME 病变的自动检测和识别中发挥重要作用。我们提出的框架擅长正确定位和分类疾病病变。与现有的 DR 和 DME 分类方法相比，我们的方法可以从低强度和嘈杂的图像中提取具有代表性的关键点并对其进行准确分类。因此，我们的方法可以在 DR 和 DME 病变的自动检测和识别中发挥重要作用。我们提出的框架擅长正确定位和分类疾病病变。与现有的 DR 和 DME 分类方法相比，我们的方法可以从低强度和嘈杂的图像中提取具有代表性的关键点并对其进行准确分类。因此，我们的方法可以在 DR 和 DME 病变的自动检测和识别中发挥重要作用。