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Diabetic Retinopathy

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Deep image mining for diabetic retinopathy screening.

Medical image analysis
Deep learning is quickly becoming the leading methodology for medical image analysis. Given a large medical archive, where each image is associated with a diagnosis, efficient pathology detectors or classifiers can be trained with virtually no expert...

Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images.

Eye (London, England)
PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five ste...

Automated Identification of Diabetic Retinopathy Using Deep Learning.

Ophthalmology
PURPOSE: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The obje...

Multimodal Imaging in Diabetic Macular Edema.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Throughout ophthalmic history it has been shown that progress has gone hand in hand with technological breakthroughs. In the past, fluorescein angiography and fundus photographs were the most commonly used imaging modalities in the management of diab...

Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order t...

Lower prevalence of proliferative diabetic retinopathy in elderly onset patients with diabetes.

Diabetes research and clinical practice
AIMS: To assess diabetic retinopathy (DR) prevalence between elderly onset and younger onset diabetes with similar diabetes duration and evaluate the association between DR prevalence and serum insulin-like growth factor 1 (IGF-1) levels.

Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Diabetic retinopathy (DR) is a progressive disease, and its detection at an early stage is crucial for saving a patient's vision. An automated screening system for DR can help in reduce the chances of complete blindness due to DR along with lowering ...

Relationship between vitamin D status and vascular complications in patients with type 2 diabetes mellitus.

Nutrition research (New York, N.Y.)
We aimed to investigate the association between serum 25-hydroxyvitamin D (25[OH]D) and microvascular complications in type 2 diabetes mellitus (T2DM) patients. It was hypothesized that lower 25(OH)D would be associated with increased microvascular c...

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that ...

Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.

Medical & biological engineering & computing
Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by build...