scholarly journals Diabetic Retinopathy detection through integration of Deep Learning classification framework

2017 ◽  
Author(s):  
Alexander Rakhlin
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
State Of The Art ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Diabetic Retinopathy Screening ◽  
Classification Framework ◽  
Ongoing Project ◽  
Retinopathy Screening

AbstractThis document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.

scholarly journals Essentials of a Robust Deep Learning System for Diabetic Retinopathy Screening: A Systematic Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Aan Chu ◽  
David Squirrell ◽  
Andelka M. Phillips ◽  
Ehsan Vaghefi
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Validation Dataset ◽  
Cochrane Library ◽  
Clinical Implementation ◽  
Diabetic Retinopathy Screening ◽  
Deep Learning Algorithm ◽  
Retinopathy Screening

This systematic review was performed to identify the specifics of an optimal diabetic retinopathy deep learning algorithm, by identifying the best exemplar research studies of the field, whilst highlighting potential barriers to clinical implementation of such an algorithm. Searching five electronic databases (Embase, MEDLINE, Scopus, PubMed, and the Cochrane Library) returned 747 unique records on 20 December 2019. Predetermined inclusion and exclusion criteria were applied to the search results, resulting in 15 highest-quality publications. A manual search through the reference lists of relevant review articles found from the database search was conducted, yielding no additional records. A validation dataset of the trained deep learning algorithms was used for creating a set of optimal properties for an ideal diabetic retinopathy classification algorithm. Potential limitations to the clinical implementation of such systems were identified as lack of generalizability, limited screening scope, and data sovereignty issues. It is concluded that deep learning algorithms in the context of diabetic retinopathy screening have reported impressive results. Despite this, the potential sources of limitations in such systems must be evaluated carefully. An ideal deep learning algorithm should be clinic-, clinician-, and camera-agnostic; complying with the local regulation for data sovereignty, storage, privacy, and reporting; whilst requiring minimum human input.

scholarly journals Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Gen-Min Lin ◽  
Mei-Juan Chen ◽  
Chia-Hung Yeh ◽  
Yu-Yang Lin ◽  
Heng-Yu Kuo ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Block Size ◽  
Detection Accuracy ◽  
Data Sets ◽  
Feature Maps ◽  
Data Set ◽  
Fundus Photographs ◽  
Grade 3

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values <0.001). The entropy image quantifies the amount of information in the fundus photograph and efficiently accelerates the generating of feature maps in the CNN. The research results draw the conclusion that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.

Deep learning approach for diabetic retinopathy screening

2016 ◽  
Vol 94 ◽  
Author(s):  
E. Colas ◽  
A. Besse ◽  
A. Orgogozo ◽  
B. Schmauch ◽  
N. Meric ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Approach ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening

2019 ◽  
Vol 501 ◽  
pp. 511-522 ◽  
Author(s):  
Tao Li ◽  
Yingqi Gao ◽  
Kai Wang ◽  
Song Guo ◽  
Hanruo Liu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Diagnostic Assessment ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

Deep Learning Methods for the Assessment of Vascular Diameters for Diabetic Retinopathy Screening

2021 ◽  
Author(s):  
Yagna Sai Kalyan Rebba ◽  
Shashank Rao Gujja ◽  
Satyanarayana Teja Siripalli ◽  
Mohammed Shoaib ◽  
Lakshmi Kala Pampana ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Methods ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

scholarly journals Automated diabetic retinopathy screening for primary care settings using deep learning

2021 ◽  
pp. 100045
Author(s):  
Alauddin Bhuiyan ◽  
Arun Govindaiah ◽  
Avnish Deobhakta ◽  
Mohd Hossain ◽  
Richard Rosen ◽  
...  
Keyword(s):  
Primary Care ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Diabetic Retinopathy Screening ◽  
Primary Care Settings ◽  
Retinopathy Screening
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