Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy
This paper mainly focuses on the deep learning application in classifying the stage of diabetic retinopathy and detecting the laterality of the eye using funduscopic images. Diabetic retinopathy is a chronic, progressive, sight-threatening disease of the retinal blood vessels. Ophthalmologists diagnose diabetic retinopathy through early funduscopic screening. Normally, there is a time delay in reporting and intervention, apart from the financial cost and risk of blindness associated with it. Using a convolutional neural network based approach for automatic diagnosis of diabetic retinopathy, we trained the prediction network on the publicly available Kaggle dataset. Approximately 35,000 images were used to train the network, which observed a sensitivity of 80.28% and a specificity of 92.29% on the validation dataset of ~53,000 images. Using 8,810 images, the network was trained for detecting the laterality of the eye and observed an accuracy of 93.28% on the validation set of 8,816 images.
Neural Networks (Computer) Diabetic Retinopathy Artificial Intelligence
Manoj Raju Venkatesh Pagidimarri Ryan Barreto Amrit Kadam Vamsichandra Kasivajjala Arun Aswath
Enlightiks Business Solutions Private Limited-a Practo Company,Bangalore,Karnataka,India
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
559-563
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）