Diagnosis of Ophthalmic Diseases in Fundus Image by Using Deep Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1842-1847
Author(s):  
Aasawari M. Patankar
Keyword(s):  
Deep Learning ◽  
Information Source ◽  
Ensemble Classifier ◽  
Machine Learning Techniques ◽  
Hypertensive Retinopathy ◽  
Fundus Image ◽  
Ocular Diseases ◽  
Eye Damage ◽  
Ophthalmic Diseases ◽  
Retinal Fundus

Abstract: In the human eye, Damage in the retina may cause ophthalmic diseases like cataracts, AMD, Hypertensive retinopathy, myopia, etc. To cure these diseases, many ophthalmologists use retinal fundus images as an important information source to find out ophthalmic diseases. Multiple techniques have been introduced for the screening of ocular diseases. Today’s world is in great demand to find out ocular diseases by using deep learning and machine learning techniques. This paper uses pre-trained deep neural networks to determine five categories of ophthalmic diseases such as cataract, AMD, Hypertensive retinopathy, myopia, and normal. Dataset is created into binary and multiclass, then trained on Resnet-101 of convolutional neural network (CNN) and evaluated. The accuracy of this model is found to be 90.38% and 88.5% for binary and multiclass respectively. Keywords: Retinal fundus image, Ocular diseases, CNN, ResNet, deep learning. Image processing, Ensemble classifier

scholarly journals Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images (Preprint)

2021 ◽  
Author(s):  
Xiuhua Guo
Keyword(s):  
Anomaly Detection ◽  
Sensitivity And Specificity ◽  
Clinical Pathways ◽  
Age Related Macular Degeneration ◽  
Hypertensive Retinopathy ◽  
Fundus Images ◽  
Ocular Diseases ◽  
Data Set ◽  
Retinal Fundus Images ◽  
Retinal Fundus

BACKGROUND The supervised deep learning approach provides state-of-the-art performance in a variety of fundus image classification tasks, but it is not applicable to screening tasks with numerous or unknown disease types. The unsupervised anomaly detection (AD) approach, which needs only normal samples to develop a model, may be a workable and cost-saving method of screening for ocular diseases. OBJECTIVE To developed and evaluated an AD model for detecting ocular diseases based on color fundus images. METHODS A generative adversarial network (GAN)–based AD method for detecting possible ocular diseases was developed and evaluated using 90499 retinal fundus images derived from four large-scale real-world data sets. Three other independent external test sets were used for external testing and further analysis of the model’s performance in detecting six common ocular diseases (diabetic retinopathy (DR), glaucoma, cataract, age-related macular degeneration (AMD), hypertensive retinopathy (HR), myopia) and DR of different severity levels. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity of the model’s performance were calculated and presented. RESULTS Our model achieved an AUC of 0.896 with 82.69% sensitivity and 82.63% specificity in detecting abnormal fundus images in the internal test set, and it achieved an AUC of 0.900 with 83.25% sensitivity and 85.19% specificity in one external proprietary data set. In the detection of six common ocular diseases, the AUCs for DR, glaucoma, cataract, AMD, HR and myopia were 0.891, 0.916, 0.912, 0.867, 0.895 and 0.961, respectively. Moreover, the AD model had an AUC of 0.868 for detecting any DR, 0.908 for detecting referable DR and 0.926 for detecting vision-threatening DR. CONCLUSIONS The AD approach achieved high sensitivity and specificity in detecting ocular diseases based on fundus images, meaning that this model might be an efficient and economical tool for optimizing current clinical pathways for ophthalmologists. In future research, it will be necessary to evaluate the practical applicability of the AD approach in ocular disease screening.

The region of interest localization for glaucoma analysis from retinal fundus image using deep learning

2018 ◽  
Vol 165 ◽  
pp. 25-35 ◽  
Author(s):  
Anirban Mitra ◽  
Priya Shankar Banerjee ◽  
Sudipta Roy ◽  
Somasis Roy ◽  
Sanjit Kumar Setua
Keyword(s):  
Deep Learning ◽  
Region Of Interest ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus

Diabetic Retinopathy Prediction Device System

2019 ◽  
Vol 16 (10) ◽  
pp. 4266-4270
Author(s):  
Meenu Garg ◽  
Sheifali Gupta ◽  
Rakesh Ahuja ◽  
Deepali Gupta
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Treatment Effect ◽  
Physiological Parameters ◽  
Early Prediction ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Image Capturing ◽  
The One ◽  
Retinal Fundus

The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.

scholarly journals Review of Machine Learning Applications Using Retinal Fundus Images

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Yeonwoo Jeong ◽  
Yu-Jin Hong ◽  
Jae-Ho Han
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Fundus Image ◽  
Learning Methods ◽  
Age Related ◽  
Automated Screening ◽  
Complex Features ◽  
Screening And Diagnosis

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

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