An imaging informatics-based system for feature extraction of retinal fundus images with deep learning algorithms

2021 ◽  
Author(s):  
Eric Manning ◽  
Yuting Gao ◽  
Veda Murthy ◽  
Emma Munch ◽  
Brent Liu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Imaging Informatics ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Predicting High Coronary Artery Calcium Score From Retinal Fundus Images With Deep Learning Algorithms

2020 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Jaemin Son ◽  
Joo Young Shin ◽  
Eun Ju Chun ◽  
Kyu-Hwan Jung ◽  
Kyu Hyung Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Coronary Artery Calcium Score ◽  
Learning Algorithms ◽  
Calcium Score ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

A deep learning framework for the detection of Plus disease in retinal fundus images of preterm infants

2021 ◽  
Author(s):  
Sivakumar Ramachandran ◽  
P. Niyas ◽  
Anand Vinekar ◽  
Renu John
Keyword(s):  
Deep Learning ◽  
Preterm Infants ◽  
Fundus Images ◽  
Learning Framework ◽  
Retinal Fundus Images ◽  
Retinal Fundus

An Explainable Deep Learning Ensemble Model for Robust Diagnosis of Diabetic Retinopathy Grading

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-24
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Vision Loss ◽  
Final Diagnosis ◽  
Learning Rate ◽  
Ensemble Model ◽  
Fundus Images ◽  
Robust Detection ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is one of the most common causes of vision loss in people who have diabetes for a prolonged period. Convolutional neural networks (CNNs) have become increasingly popular for computer-aided DR diagnosis using retinal fundus images. While these CNNs are highly reliable, their lack of sufficient explainability prevents them from being widely used in medical practice. In this article, we propose a novel explainable deep learning ensemble model where weights from different models are fused into a single model to extract salient features from various retinal lesions found on fundus images. The extracted features are then fed to a custom classifier for the final diagnosis of DR severity level. The model is trained on an APTOS dataset containing retinal fundus images of various DR grades using a cyclical learning rates strategy with an automatic learning rate finder for decaying the learning rate to improve model accuracy. We develop an explainability approach by leveraging gradient-weighted class activation mapping and shapely adaptive explanations to highlight the areas of fundus images that are most indicative of different DR stages. This allows ophthalmologists to view our model's decision in a way that they can understand. Evaluation results using three different datasets (APTOS, MESSIDOR, IDRiD) show the effectiveness of our model, achieving superior classification rates with a high degree of precision (0.970), sensitivity (0.980), and AUC (0.978). We believe that the proposed model, which jointly offers state-of-the-art diagnosis performance and explainability, will address the black-box nature of deep CNN models in robust detection of DR grading.

Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images

2020 ◽  
Vol 217 ◽  
pp. 121-130 ◽  
Author(s):  
Jooyoung Chang ◽  
Ahryoung Ko ◽  
Sang Min Park ◽  
Seulggie Choi ◽  
Kyuwoong Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiovascular Mortality ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Detection of anaemia from retinal fundus images via deep learning

2019 ◽  
Vol 4 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Akinori Mitani ◽  
Abigail Huang ◽  
Subhashini Venugopalan ◽  
Greg S. Corrado ◽  
Lily Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

1999 ◽  
Vol 3 (2) ◽  
pp. 125-138 ◽  
Author(s):  
Ali Can ◽  
Hong Shen ◽  
Turner ◽  
Tanenbaum ◽  
Roysam
Keyword(s):  
Feature Extraction ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Automated Tracing
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