Diabetic Retinopathy Detection using Deep Convolutional Neural Network with Visualization of Guided Grad-CA

R. H. Paradisa, A. Bustamam, A. Victor, A. Yudantha, Devvi Sarwinda
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引用次数: 2

Abstract

One of the complications of diabetes that represents a serious threat to world health is Diabetic Retinopathy (DR). High blood sugar levels in people with diabetes can damage the blood vessels in the retina and causing blindness. DR can be detected by examining the fundus image by an ophthalmologist. However, the limited number of ophthalmologists who can analyze fundus image is an obstacle because the number of DR sufferers continues to increase. Therefore, an automated system is needed to help doctors diagnose the disease. Researchers have developed deep learning techniques as Artificial Intelligence (AI) approach to finding DR in fundus images. In this research, we use the Deep Convolutional Neural Networks method with InceptionV3 structure and various optimizers such as the Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square Propagation (RMSprop), and Adaptive Moment Estimation (Adam). The fundus image dataset previously through the augmentation and preprocessing steps to make it easier for the model to recognize the image. The InceptionV3 model with the Adam optimizer gave the best results in detecting DR lesions from the Kaggle dataset with 96% accuracy. This paper also presents a Grad-CAM guided activation map that can describe the position of the suspicious lesion to explain the results of DR detection.
基于视觉引导的深度卷积神经网络检测糖尿病视网膜病变
糖尿病视网膜病变是对世界健康构成严重威胁的糖尿病并发症之一。糖尿病患者的高血糖水平会损害视网膜血管,导致失明。DR可以由眼科医生通过检查眼底图像来检测。但是,能够分析眼底图像的眼科医生数量有限,这是一个障碍,因为DR患者不断增加。因此,需要一个自动化系统来帮助医生诊断这种疾病。研究人员开发了深度学习技术作为人工智能(AI)方法来发现眼底图像中的DR。在本研究中,我们使用了具有InceptionV3结构的深度卷积神经网络方法和各种优化器,如随机梯度下降动量(SGDM),均方根传播(RMSprop)和自适应矩估计(Adam)。眼底图像数据集之前通过增强和预处理步骤,使模型更容易识别图像。带有Adam优化器的InceptionV3模型在检测Kaggle数据集的DR病变方面给出了最佳结果,准确率为96%。本文还提出了一个Grad-CAM引导的激活图,可以描述可疑病变的位置,以解释DR检测的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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