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.

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

scholarly journals Analysis and Design of Deep Learning Algorithms for Retinal Image Classification for Early Detection of Diabetic Retinopathy

2021 ◽  
Vol 12 (6) ◽  
pp. 2633-2641
Author(s):  
Amita Meshram, Dr. Deepak Dembla, Dr. Reema Ajmera
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Retinal Image ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Clinical Images ◽  
Analysis And Design ◽  
Deep Learning Algorithm ◽  
Image Investigation ◽  
Clinical Determination

This paper presents a far reaching survey of the standard and use of deep learning in retinal image investigation. Many eye ailments regularly lead to visual impairment without legitimate clinical determination and clinical treatment. For instance, diabetic retinopathy (DR) is one such illness in which the retinal veins of natural eyes are harmed. The ophthalmologists analyze DR dependent on their expert information that is work escalated. With the advances in image preparing and man-made reasoning, Personal Computer vision-based methods have been applied quickly and broadly in the field of clinical images investigation. The important deep learning algorithms such as CNN Convolution Neural Network, ConvNet based algorithm, LCD net and Deep CNN, their working and main features of some of these standard  Deep Learning algorithm are analyzed in detailed. Proposed algorithm will become more reliable accurate by introducing new features as well as better quality input by using advance algorithm of image processing.  

scholarly journals A64 Viral sequence classification using deep learning algorithms

2019 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
David Nieuwenhuijse ◽  
Bas Oude Munnink ◽  
My Phan ◽  
Marion Koopmans
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
High Sensitivity ◽  
Current Approach ◽  
Metagenomic Sequencing ◽  
Public Health Importance ◽  
Short Reads ◽  
Deep Learning Algorithm ◽  
High Threat

Abstract Sewage samples have a high potential benefit for surveillance of circulating pathogens because they are easy to obtain and reflect population-wide circulation of pathogens. These type of samples typically contain a great diversity of viruses. Therefore, one of the main challenges of metagenomic sequencing of sewage for surveillance is sequence annotation and interpretation. Especially for high-threat viruses, false positive signals can trigger unnecessary alerts, but true positives should not be missed. Annotation thus requires high sensitivity and specificity. To better interpret annotated reads for high-threat viruses, we attempt to determine how classifiable they are in a background of reads of closely related low-threat viruses. As an example, we attempted to distinguish poliovirus reads, a virus of high public health importance, from other enterovirus reads. A sequence-based deep learning algorithm was used to classify reads as either polio or non-polio enterovirus. Short reads were generated from 500 polio and 2,000 non-polio enterovirus genomes as a training set. By training the algorithm on this dataset we try to determine, on a single read level, which short reads can reliably be labeled as poliovirus and which cannot. After training the deep learning algorithm on the generated reads we were able to calculate the probability with which a read can be assigned to a poliovirus genome or a non-poliovirus genome. We show that the algorithm succeeds in classifying the reads with high accuracy. The probability of assigning the read to the correct class was related to the location in the genome to which the read mapped, which conformed with our expectations since some regions of the genome are more conserved than others. Classifying short reads of high-threat viral pathogens seems to be a promising application of sequence-based deep learning algorithms. Also, recent developments in software and hardware have facilitated the development and training of deep learning algorithms. Further plans of this work are to characterize the hard-to-classify regions of the poliovirus genome, build larger training databases, and expand on the current approach to other viruses.

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 Indoor and Outdoor Fire Localization Using YOLO Algorithm

2021 ◽  
Vol 2114 (1) ◽  
pp. 012067
Author(s):  
Ruba R. Nori ◽  
Rabah N. Farhan ◽  
Safaa Hussein Abed
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Fire Detection ◽  
Time Data ◽  
Deep Learning Algorithm ◽  
Positive Elements ◽  
Image Dataset

Abstract Novel algorithm for fire detection has been introduced. CNN based System localization of fire for real time applications was proposed. Deep learning algorithms shows excellent results in a way that it accuracy reaches very high accuracy for fire image dataset. Yolo is a superior deep learning algorithm that is capable of detect and localize fires in real time. The luck of image dataset force us to limit the system in binary classification test. Proposed model was tested on dataset gathered from the internet. In this article, we built an automated alert system integrating multiple sensors and state-of-the art deep learning algorithms, which have a limited number of false positive elements and which provide our prototype robot with reasonable accuracy in real-time data and as little as possible to track and record fire events as soon as possible.

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
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