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首页> 外文会议>IEEE International Conference on Robotics, Automation and Artificial Intelligence >DiaRet: A Browser-Based Application for the Grading of Diabetic Retinopathy with Integrated Gradients
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DiaRet: A Browser-Based Application for the Grading of Diabetic Retinopathy with Integrated Gradients

机译：腹泻：基于浏览器的浏览器，用于糖尿病患者与综合梯度分级

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Patients with long-standing diabetes often fall prey to Diabetic Retinopathy (DR) resulting in changes in the retina of the human eye, which may lead to loss of vision in extreme cases. The aim of this study is two-fold: (a) create deep learning models that were trained to grade degraded retinal fundus images and (b) to create a browser-based application that will aid in diagnostic procedures by highlighting the key features of the fundus image. In this research work, we have emulated the images plagued by distortions by degrading the images based on multiple different combinations of Light Transmission Disturbance, Image Blurring and insertion of Retinal Artifacts. InceptionV3, ResNet-50 and InceptionResNetV2 were trained and used to classify retinal fundus images based on their severity level and then further used in the creation of a browser-based application, which implements the Integration Gradient (IG) Attribution Mask on the input image and demonstrates the predictions made by the model and the probability associated with each class.
机译：长期糖尿病的患者常常落入糖尿病视网膜病变（DR）导致人眼视网膜的变化，这可能导致极端情况下失去视力。本研究的目的是两倍：（a）创建深入学习模型，该模型被培训到级别降级的视网膜眼底图像和（b）来创建基于浏览器的应用程序，这将通过突出显示的主要特征来帮助诊断程序。眼底图像。在这项研究工作中，我们通过基于光传输干扰，图像模糊和视网膜伪影的多种不同组合来降解图像来模拟失真困扰的图像。 Inceptionv3，Reset-50和InceptionResNetv2培训并用于基于其严重性级别对视网膜基底图像进行分类，然后在创建基于浏览器的应用程序中，该应用程序实现输入图像上的集成梯度（IG）归因掩码和展示模型所做的预测和与每个类相关的概率。

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