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- Outlines a detailed overview of diabetic retinopathy, symptoms, causes and screening methodologies
- Presents a deep learning approach to automatically detect diabetic retinopathy from captured retina image
- Demonstrates a higher prediction rate of diabetic retinopathy and efficiency in early detection
This book discusses a detailed overview of diabetic retinopathy, symptoms, causes, and screening methodologies. Using a deep convolution neural network and visualizations techniques, this work develops a prognosis system used to automatically detect the diabetic retinopathy disease from captured retina images and help improve the prediction rate of diagnosis. This book gives the readers an understanding of the diabetic retinopathy disease and recognition process that helps to improve the clinical analysis efficiency. It caters to general ophthalmologists and optometrists, diabetologists, and internists who encounter diabetic patients and most prevalent retinal diseases daily. Table of Contents- Introduction
- Background of diabetic retinopathy.-
- Classification of diabetic retinopathy.-
- Deep convolutional neural network architecture.-
- Deep convolutional neural network applications and visualization tools.-
- Multi-platform deployment for prognosis system.-
- Case Studies for diabetic retinopathy with a deep learning system.
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