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|z 9780128174401
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|z (OCoLC)1149268741
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|a 617.735
|2 23
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|a Diabetes and fundus OCT /
|c edited by Ayman S. El-Baz and Jasjit S. Suri.
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| 260 |
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|a Amsterdam :
|b Elsevier,
|c 2020.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
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|a Computer-assisted diagnosis ;
|v v. 1
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| 500 |
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|a Includes index.
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| 588 |
0 |
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|a Print version record.
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| 505 |
0 |
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|a Intro -- Diabetes and Fundus OCT -- Copyright -- Contents -- Contributors -- Chapter 1: Computer-aided diagnosis system based on a comprehensive local features analysis for early diabetic retinopath ... -- 1. Introduction -- 2. Materials and methods -- 2.1. Contrast enhancement and noise elimination -- 2.2. Vessel segmentation -- 2.3. Local feature extraction and diagnosis -- 2.3.1. Blood vessel density estimation -- 2.3.2. Retinal blood vessel caliber -- 2.3.3. Width of the FAZ -- 2.3.4. Bifurcation points -- 2.4. Mild DR diagnosis -- 3. Experimental results -- 4. Conclusions -- References
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| 505 |
8 |
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|a Chapter 2: Deep learning approach for classification of eye diseases based on color fundus images -- 1. Introduction -- 2. Deep learning -- 2.1. Convolutional neural network -- 3. Fundus imaging -- 3.1. Image acquisition -- 3.2. Main vascular abnormalities -- 3.2.1. Tortuosity -- 3.2.2. Generalized arteriolar narrowing -- 3.2.3. Focal arteriolar narrowing -- 3.2.4. Bifurcations abnormalities -- 3.2.5. Crossing abnormalities -- 3.3. Main nonvascular -- 3.3.1. Microaneurysms and red dots -- 3.3.2. Hemorrhages -- 3.3.3. Hard exudates -- 3.3.4. Cotton wool spots -- 3.4. Hypertensive retinopathy
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| 505 |
8 |
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|a 3.5. Diabetic retinopathy -- 4. Research method -- 4.1. Research framework -- 4.2. Dataset -- 4.3. Preprocessing -- 4.4. Classifier configuration -- 4.5. Model training and cross validation -- 4.6. Experiment result -- 4.7. Conclusion -- References -- Further reading -- Chapter 3: Fundus retinal image analyses for screening and diagnosing diabetic retinopathy, macular edema, and glaucoma d ... -- 1. Introduction -- 2. A brief history of fundus retinal imaging -- 3. Public retinal image databases -- 4. Automatic retinal image analysis -- 4.1. Performance metrics used in ARIA
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| 505 |
8 |
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|a 4.2. Retinal vessel segmentation -- 4.3. Optic disc (or ONH), macula and fovea: Detection and segmentation -- 5. Macular edema and DR classification using fundus image -- 5.1. Detection of microaneurysms and hemorrhage -- 5.2. Detection of exudates -- 5.3. Automatic classification of DR and DME severity levels -- 6. Glaucoma classification using ARIA -- 7. Future trends in fundus retinal imaging -- 8. Conclusion -- References -- Chapter 4: Mobile phone-based diabetic retinopathy detection system -- 1. Introduction -- 1.1. Causes of diabetic retinopathy -- 1.2. Types of diabetic retinopathy
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| 505 |
8 |
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|a 2. Related works -- 3. Proposed system -- 3.1. Artificial neural networks -- 3.2. Discrete wavelet transform -- 3.3. Smartphone as an ophthalmoscope -- 3.4. Software requirement and description -- 3.5. Algorithm and flow chart -- 4. Experimental results -- 5. Conclusion -- References -- Chapter 5: Computer-aided diagnosis of age-related macular degeneration by OCT, fundus image analysis -- 1. Introduction -- 1.1. Risk factors -- 1.2. Symptoms of macular degeneration -- 1.2.1. Macular degeneration diagnosis, treatment, and prevention -- 1.3. Diagnosis -- 1.4. AMD treatment
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| 650 |
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0 |
|a Diabetic retinopathy
|x Diagnosis.
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| 650 |
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0 |
|a Retina
|x Tomography.
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| 650 |
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6 |
|a R�etine
|0 (CaQQLa)201-0014677
|x Tomographie.
|0 (CaQQLa)201-0379836
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| 650 |
|
7 |
|a Diabetic retinopathy
|x Diagnosis.
|2 fast
|0 (OCoLC)fst00892232
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| 700 |
1 |
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|a El-Baz, Ayman S.
|
| 700 |
1 |
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|a Suri, Jasjit S.
|
| 776 |
0 |
8 |
|i Print version:
|z 9780128174418
|
| 776 |
0 |
8 |
|i Print version:
|t Diabetes and fundus OCT.
|d Amsterdam : Elsevier, 2020
|z 0128174404
|z 9780128174401
|w (OCoLC)1111971925
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128174401
|z Texto completo
|
