Diabetes and Fundus OCT (Computer-Assisted Diagnosis) 1st edition – Ebook PDF Instant Delivery – ISBN(s): 9780128174401,0128174404

Product details:

• ISBN-10 ‏ : ‎ 0128174404
• ISBN-13 ‏ : ‎ 978-0128174401
• Author: Ayman S. El-Baz, Jasjit Suri

Diabetes and Fundus OCT brings together a stellar cast of authors who review the computer-aided diagnostic (CAD) systems developed to diagnose non-proliferative diabetic retinopathy in an automated fashion using Fundus and OCTA images. Academic researchers, bioengineers, new investigators and students interested in diabetes and retinopathy need an authoritative reference to bring this multidisciplinary field together to help reduce the amount of time spent on source-searching and instead focus on actual research and the clinical application. This reference depicts the current clinical understanding of diabetic retinopathy, along with the many scientific advances in understanding this condition.

Table contents:

1. Computer Aided Diagnosis System Based on a Comprehensive Local Features Analysis for Early Diabetic Retinopathy Detection using OCTA

2. Deep Learning Approach for Classification of Eye Diseases Based on Color Fundus Images

3. Fundus Retinal Image Analyses for Screening and Diagnosing Diabetic Retinopathy, Macular edema, and Glaucoma Disorders

4. Mobile Phone Based Diabetic Retinopathy Detection System

5. Computer Aided Diagnosis of Age Related Macular Degeneration by OCT, Fundus Image Analysis

6. Retinal Diseases Diagnosis Based on Optical Coherence Tomography Angiography (OCTA)

7. Optical Coherence Tomography: A Review

8. An Accountable Saliency-Oriented Data-Driven Approach to Diabetic Retinopathy Detection

9. Machine Learning Based Abnormalities Detection In Retinal Fundus Images

10. Optical Coherence Tomography Angiography of Retinal Vascular Diseases

11. Screening of The Diabetic Retinopathy In Engineering

12. Optical Coherence Tomography Angiography In Type 3 Neovascularization

13. Diabetic Retinopathy Detection in Ocular Images by Dictionary Learning

14. Lesion Detection Using Segmented Structure Of Retina

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