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Microaneurysm

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Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer ...

Microaneurysm detection in fundus images using a two-step convolutional neural network.

Biomedical engineering online
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since micro...

Retinopathy Analysis Based on Deep Convolution Neural Network.

Advances in experimental medicine and biology
At medical checkups or mass screenings, the fundus examination is effective for early detection of systemic hypertension, arteriosclerosis, diabetic retinopathy, etc. In most cases, ophthalmologists and physicians grade retinal images by the conditio...

Microaneurysms detection in color fundus images using machine learning based on directional local contrast.

Biomedical engineering online
BACKGROUND: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and re...

Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.

Current eye research
: To describe the development and validation of an artificial intelligence-based, deep learning algorithm (DeepDR) for the detection of diabetic retinopathy (DR) in retinal fundus photographs. : Five hundred fundus images, which had detailed labellin...

Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.

Translational vision science & technology
PURPOSE: Heatmapping techniques can support explainability of deep learning (DL) predictions in medical image analysis. However, individual techniques have been mainly applied in a descriptive way without an objective and systematic evaluation. We in...

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Proceedings of the National Academy of Sciences of the United States of America
Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro method...

Deep Learning Approach for Automatic Microaneurysms Detection.

Sensors (Basel, Switzerland)
In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means t...

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

Translational vision science & technology
PURPOSE: Accurate segmentation of microaneurysms (MAs) from adaptive optics scanning laser ophthalmoscopy (AOSLO) images is crucial for identifying MA morphologies and assessing the hemodynamics inside the MAs. Herein, we introduce AOSLO-net to perfo...

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...