AIMC Topic: Diabetic Retinopathy

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Optimising deep learning models for ophthalmological disorder classification.

Scientific reports
Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structura...

DAU-Net: a novel U-Net with dual attention for retinal vessel segmentation.

Biomedical physics & engineering express
In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped n...

Deep learning generalization for diabetic retinopathy staging from fundus images.

Physiological measurement
. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due...

Early Prediction of Cardio Vascular Disease (CVD) from Diabetic Retinopathy using improvised deep Belief Network (I-DBN) with Optimum feature selection technique.

BMC cardiovascular disorders
Cardio Vascular Disease (CVD) is one of the leading causes of mortality and it is estimated that 1 in 4 deaths happens due to it. The disease prevalence rate becomes higher since there is an inadequate system/model for predicting CVD at an earliest. ...

ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburd...

Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.

Eye (London, England)
PURPOSE: This study aims to develop a deep-learning-based software capable of detecting and differentiating microaneurysms (MAs) as hyporeflective or hyperreflective on structural optical coherence tomography (OCT) images in patients with non-prolife...

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.

Scientific reports
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it cha...

Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading.

IEEE journal of biomedical and health informatics
Diabetic Retinopathy (DR) is a leading cause of vision loss around the world. To help diagnose it, numerous cutting-edge works have built powerful deep neural networks (DNNs) to automatically grade DR via retinal fundus images (RFIs). However, RFIs a...

Empirical analysis on retinal segmentation using PSO-based thresholding in diabetic retinopathy grading.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Diabetic retinopathy (DR) is associated with long-term diabetes and is a leading cause of blindness if it is not diagnosed early. The rapid growth of deep learning eases the clinicians' DR diagnosing procedure. It automatically extracts t...

Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer.

Frontiers in public health
INTRODUCTION: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is...