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Diabetic Retinopathy

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A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Journal of medical systems
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automa...

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

PloS one
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNe...

An ensemble deep learning based approach for red lesion detection in fundus images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical ...

Learning ensemble classifiers for diabetic retinopathy assessment.

Artificial intelligence in medicine
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...

Diabetic retinopathy screening using deep neural network.

Clinical & experimental ophthalmology
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening.

Reconstructing cell cycle and disease progression using deep learning.

Nature communications
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progressi...

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

Journal of medical engineering & technology
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection ...

Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy.

PloS one
PURPOSE: Disease staging involves the assessment of disease severity or progression and is used for treatment selection. In diabetic retinopathy, disease staging using a wide area is more desirable than that using a limited area. We investigated if d...

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

Biomedical engineering online
BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well...

Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms.

Irish journal of medical science
BACKGROUND: The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. ...