This comprehensive compendium designs deep neural network models and systems for intelligent analysis of fundus imaging. In response to several blinding fundus diseases such as Retinopathy of Prematurity (ROP), Diabetic Retinopathy (DR) and Macular Edema (ME), different image acquisition devices and fundus image analysis tasks are elaborated.
From the actual fundus disease analysis tasks, various deep neural network models and experimental results are constructed and analyzed. For each task, an actual system for clinical application is developed.
This useful reference text provides theoretical and experimental reference basis for AI researchers, system engineers of intelligent medicine and ophthalmologists.
Contents:
- Introduction
- Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks
- Deep ROP: An Automated ROP Screening System
- Diagnosis of Diabetic Retinopathy Using Deep Neural Networks
- Automated Identification and Grading System of Diabetic Retinopathy Using Deep Neural Networks
- Automated Segmentation of Macular Edema in OCT Using Deep Neural Networks
- Deep UWF: An Automated Ultrawide-field Fundus Screening System via Deep Learning
- Deep UWF-Plus: Automatic Fundus Identification and Diagnosis System Based on Ultrawide-field Fundus Imaging
Readership: Researchers, professionals, academics and graduate students in neural networks and machine learning.
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