Peter H. Scanlon MD, MRCOphth, DCH, FRCP Consultant Ophthalmologist Lecturer’s research while affiliated with University of Oxford and other places

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Publications (15)


Low Vision and Blindness from Diabetic Retinopathy
  • Chapter

March 2009

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14 Reads

Peter H. Scanlon MD, MRCOphth, DCH, FRCP Consultant Ophthalmologist Lecturer

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Charles P. Wilkinson MD Chairman Professor of Ophthalmology

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FBIPP Gloucestershire Education and Research Development Manager Stephen J. Aldington DMS

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David R. Matthews MA (Oxon), DPhil, BM, BCh, FRCP Professor Chairman

Definitions of BlindnessCosts of Blindness Due to Diabetic RetinopathyCosts of Treatment for Diabetic RetinopathyReduced Vision and Quality of LifeLow Vision RehabilitationPractice PointsReferences


Other Retinal Conditions that are More Frequent in Diabetes
  • Chapter
  • Full-text available

March 2009

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18 Reads

HypertensionRetinal Arterial OcclusionsRetinal Venous OcclusionsAnterior Ischaemic Optic NeuropathyAsteroid HyalosisMacroaneurysmPractice PointReferences

Download


Stage R3: Proliferative Diabetic Retinopathy and Advanced Diabetic Retinopathy

March 2009

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10 Reads

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1 Citation

The Relevant AnatomyThe Photographic AppearanceFluorescein Angiographic AppearancePresentationThe ‘Early Worsening’ PhenomenonMultidisciplinary Approach and Treatment of Associated Risk FactorsLaser Treatment for Proliferative Diabetic Retinopathy (see Fig. 9.2c)Anti-VEGF TreatmentsVitreous Haemorrhage Obscuring the Retinal ViewPractice PointsReferences



A Practical Manual of Diabetic Retinopathy Management

March 2009

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25 Reads

Moderate and Severe NPDR (ETDRS and International)1 and Preproliferative DR (UK Screening)2Retinal HaemorrhagesIntraretinal Microvascular AbnormalityVenous BeadingPractice PointsReferences


Stage R0: The Normal Eye

March 2009

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19 Reads


A Practical Manual of Diabetic Retinopathy Management

March 2009

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245 Reads




Citations (1)


... They applied data augmentation approaches to build many chest X-ray pictures artificially. According to experimental data, the proposed models categorized chest X-ray photographs as normal or COVID-19 with an accuracy of 97.20 percent for Resnet50, 98.10 percent for InceptionV3, and 98.30 percent for VGG16 [16,17]. The data imply that transfer learning is successful, with great performance and straightforward COVID-19 detection methodologies. ...

Reference:

A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance
Stage R3: Proliferative Diabetic Retinopathy and Advanced Diabetic Retinopathy
  • Citing Chapter
  • March 2009