论文标题
基于转移学习的糖尿病性视网膜病分级系统
Diabetic Retinopathy Grading System Based on Transfer Learning
论文作者
论文摘要
研究人员正在做出许多努力，以便准确地自动检测和诊断糖尿病性视网膜病（DR）。该疾病非常危险，因为如果不连续筛查，可能会突然引起失明。因此，许多计算机辅助诊断（CAD）系统已开发出来诊断各种DR等级。最近，已经采用了许多基于深度学习（DL）方法的CAD系统，以获得深度学习优点，以诊断DR疾病的病理异常。在本文中，我们根据多标签分类提供一个完整的DL CAD系统。在拟议的DL CAD系统中，我们提出了一种定制的ExcilityNet模型，以诊断DR疾病的早期和高级等级。学习转移在培训小型数据集方面非常有用。我们使用IDRID数据集。它是一个多标签数据集。实验表明，所提出的DL CAD系统具有牢固，可靠，并且可以在检测和分级DR方面进行有希望的结果。提出的系统达到的准确性（ACC）等于86％，骰子相似系数（DSC）等于78.45。
Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD systems based on deep learning (DL) methods have been adopted to get deep learning merits in diagnosing the pathological abnormalities of DR disease. In this paper, we present a full based-DL CAD system depending on multi-label classification. In the proposed DL CAD system, we present a customized efficientNet model in order to diagnose the early and advanced grades of the DR disease. Learning transfer is very useful in training small datasets. We utilized IDRiD dataset. It is a multi-label dataset. The experiments manifest that the proposed DL CAD system is robust, reliable, and deigns promising results in detecting and grading DR. The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity coefficient (DSC) equals 78.45.
