论文标题
基于智能手机的自动化系统，用于诊断糖尿病性视网膜病变
Automated Smartphone based System for Diagnosis of Diabetic Retinopathy
论文作者
论文摘要
早期诊断为治疗该疾病的糖尿病性视网膜病一直未能吸引居住在农村地区的糖尿病患者。缺乏训练有素的眼科医生，医疗保健中心的可用性有限以及诊断设备的付出能力。尽管文献中已经实施了许多基于学习的自动诊断糖尿病性视网膜病变技术，但这些方法仍然无法提供护理点诊断。这增加了对非专家可以使用的糖尿病性视网膜病的独立诊断。最近，全球智能手机的使用一直在增加。可以在智能手机上部署自动诊断糖尿病性视网膜病，以便为居住在偏远地区的糖尿病患者提供即时诊断。在本文中，已经提出并实施了基于Inception的卷积神经网络和基于二进制决策的分类器的集合，以检测和分类糖尿病性视网膜病。提出的方法进一步进口到智能手机应用程序中，用于基于移动的分类，该应用程序提供了一个离线和自动系统，用于诊断糖尿病性视网膜病变。
Early diagnosis of diabetic retinopathy for treatment of the disease has been failing to reach diabetic people living in rural areas. Shortage of trained ophthalmologists, limited availability of healthcare centers, and expensiveness of diagnostic equipment are among the reasons. Although many deep learning-based automatic diagnosis of diabetic retinopathy techniques have been implemented in the literature, these methods still fail to provide a point-of-care diagnosis. This raises the need for an independent diagnostic of diabetic retinopathy that can be used by a non-expert. Recently the usage of smartphones has been increasing across the world. Automated diagnoses of diabetic retinopathy can be deployed on smartphones in order to provide an instant diagnosis to diabetic people residing in remote areas. In this paper, inception based convolutional neural network and binary decision tree-based ensemble of classifiers have been proposed and implemented to detect and classify diabetic retinopathy. The proposed method was further imported into a smartphone application for mobile-based classification, which provides an offline and automatic system for diagnosis of diabetic retinopathy.
