AIMC Topic: Microaneurysm
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Deep learning model for automatic detection of different types of microaneurysms in diabetic retinopathy.
Eye (London, England)
Jan 9, 2025
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...
Deep Learning Approach for Automatic Microaneurysms Detection.
Sensors (Basel, Switzerland)
Jan 11, 2022
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...
Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.
Translational vision science & technology
Dec 29, 2020
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...
Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
Current eye research
May 15, 2020
: 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...
Microaneurysms detection in color fundus images using machine learning based on directional local contrast.
Biomedical engineering online
Apr 15, 2020
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...
Microaneurysm detection in fundus images using a two-step convolutional neural network.
Biomedical engineering online
May 29, 2019
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...
Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms.
BMC ophthalmology
Nov 6, 2018
BACKGROUND: Convolution neural networks have been considered for automatic analysis of fundus images to detect signs of diabetic retinopathy but suffer from low sensitivity.
Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods.
IEEE transactions on nanobioscience
May 24, 2018
Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR. In th...
Microaneurysm detection using fully convolutional neural networks.
Computer methods and programs in biomedicine
Feb 22, 2018
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automat...
An ensemble deep learning based approach for red lesion detection in fundus images.
Computer methods and programs in biomedicine
Oct 14, 2017
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 ...
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