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Diabetic Retinopathy

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An automated unsupervised deep learning-based approach for diabetic retinopathy detection.

Medical & biological engineering & computing
As per the International Diabetes Federation (IDF) report, 35-60% of people suffering from diabetic retinopathy (DR) have a history of diabetes. DR is one of the primary reasons for blindness and visual impairment worldwide among adults aged 24-74 ye...

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.

Computational intelligence and neuroscience
Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and cl...

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.

Sensors (Basel, Switzerland)
Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding pro...

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

BMC ophthalmology
BACKGROUND: To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiograp...

Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.

Computers in biology and medicine
Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that ...

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.

IEEE journal of biomedical and health informatics
The fovea centralis is an essential landmark in the retina where the photoreceptor layer is entirely composed of cones responsible for sharp, central vision. The localization of this anatomical landmark in optical coherence tomography (OCT) volumes i...

Semi-supervised classification of fundus images combined with CNN and GCN.

Journal of applied clinical medical physics
PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and diffe...

Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework.

Computational and mathematical methods in medicine
OBJECTIVE: As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the ...

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection.

Frontiers in public health
Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme L...