scholarly journals In-Person Verification of Deep Learning Algorithm for Diabetic Retinopathy Screening Using Different Techniques Across Fundus Image Devices

2021 ◽  
Vol 10 (13) ◽  
pp. 17
Author(s):  
Nida Wongchaisuwat ◽  
Adisak Trinavarat ◽  
Nuttawut Rodanant ◽  
Somanus Thoongsuwan ◽  
Nopasak Phasukkijwatana ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Fundus Image ◽  
Diabetic Retinopathy Screening ◽  
Deep Learning Algorithm ◽  
Person Verification ◽  
Retinopathy Screening

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 Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

2021 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Candra Dewi ◽  
Andri Santoso ◽  
Indriati Indriati ◽  
Nadia Artha Dewi ◽  
Yoke Kusuma Arbawa
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Color Space ◽  
Training Data ◽  
Color Spaces ◽  
Deep Learning Algorithm ◽  
Optimal Accuracy

<p>Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit <em>diabetic retinophaty</em>. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi <em>diabetic retinophaty</em> adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra <em>fundus</em> retina dan algoritme <em>Convolutional Neural Network</em> (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Increasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy

Ophthalmology ◽  
2019 ◽  
Vol 126 (4) ◽  
pp. 552-564 ◽  
Author(s):  
Rory Sayres ◽  
Ankur Taly ◽  
Ehsan Rahimy ◽  
Katy Blumer ◽  
David Coz ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

scholarly journals Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis

2016 ◽  
Vol 10 (2) ◽  
pp. 254-261 ◽  
Author(s):  
Malavika Bhaskaranand ◽  
Chaithanya Ramachandra ◽  
Sandeep Bhat ◽  
Jorge Cuadros ◽  
Muneeswar Gupta Nittala ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Fundus Image ◽  
Diabetic Retinopathy Screening ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Retinopathy Screening

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 Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

2019 ◽  
Vol 137 (9) ◽  
pp. 987 ◽  
Author(s):  
Varun Gulshan ◽  
Renu P. Rajan ◽  
Kasumi Widner ◽  
Derek Wu ◽  
Peter Wubbels ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm
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