scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

scholarly journals Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 2021 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Main Idea ◽  
Diabetic Patients ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Enormous Number ◽  
Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

The Diagnosis Of Diabetic Retinopathy By Means Of Transfer Learning And Fine-Tuned Dense Layer Pipeline

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 68-72
Author(s):  
Abdulaziz Abdo Salman ◽  
Ismail Mohd Khairuddin ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman
Keyword(s):  
Blood Flow ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Transfer Learning ◽  
The Body ◽  
Dense Layer ◽  
Fundus Images ◽  
Detection Approach ◽  
Deep Learning Model

Diabetes is a global disease that occurs when the body is disabled pancreas to secrete insulin to convert the sugar to power in the blood. As a result, some tiny blood vessels on the part of the body, such as the eyes, are affected by high sugar and cause blocking blood flow in the vessels, which is called diabetic retinopathy.  This disease may lead to permanent blindness due to the growth of new vessels in the back of the retina causing it to detach from the eyes. In 2016, 387 million people were diagnosed with Diabetic retinopathy, and the number is growing yearly, and the old detection approach becomes worse. Therefore, the purpose of this paper is to computerize the old method of detecting different classes of DR from 0-4 according to severity by given fundus images. The method is to construct a fine-tuned deep learning model based on transfer learning with dense layers. The used models here are InceptionV3, VGG16, and ResNet50 with a sharpening filter. Subsequently, InceptionV3 has achieved 94% as the highest accuracy among other models.  

Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy

2021 ◽  
Vol 68 ◽  
pp. 102600
Author(s):  
Sraddha Das ◽  
Krity Kharbanda ◽  
Suchetha M ◽  
Rajiv Raman ◽  
Edwin Dhas D
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Image Features ◽  
Fundus Image

scholarly journals A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

2021 ◽  
Author(s):  
melissa de la pava ◽  
Hernan Rios ◽  
Francisco J. Rodríguez ◽  
Oscar J. Perdomo ◽  
Fabio A. González
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Model ◽  
Lesion Detection ◽  
Fundus Images ◽  
Retinal Lesion ◽  
Deep Learning Model

scholarly journals Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Mexico: Independent and Assistive use Cases (Preprint)

2020 ◽  
Author(s):  
Alejandro Noriega ◽  
Daniela Meizner ◽  
Dalia Camacho ◽  
Jennifer Enciso ◽  
Hugo Quiroz-Mercado ◽  
...  
Keyword(s):  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Sensitivity And Specificity ◽  
Use Cases ◽  
Image Analysis System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

BACKGROUND The automated screening of patients at risk of developing diabetic retinopathy (DR) represents an opportunity to improve their mid-term outcome, and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. OBJECTIVE The present study, aims to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, the performance of the automated retina image analysis (ARIA) system is evaluated under an independent scheme (i.e. only ARIA screening) and two assistive schemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the three schemes. METHODS A randomized controlled experiment was performed where seventeen ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the retina image classification of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of three retina specialists for each fundus image. RESULTS The ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC) of 98.0% and a sensitivity and specificity of 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, and 90.0% respectively for Mexican patient-cases. The results achieved outperformed the average performance of the seventeen ophthalmologists enrolled in the study. Additionally, the achieved results suggest that the ARIA system can be useful as an assistive tool, as significant sensitivity improvements were observed in the experimental condition where ophthalmologists were exposed to the ARIA’s system answer previous to their own classification (93.3%), compared to the sensitivity of the condition where participants assessed the images independently (87.3%). CONCLUSIONS These results demonstrate that both use cases of the ARIA system, independent and assistive, present a substantial opportunity for Latin American countries like Mexico, towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

scholarly journals EfficientNetB3 Architecture for Diabetic Retinopathy Assessment using  Fundus Images

2021 ◽  
Author(s):  
Abdelali ELMOUFIDI ◽  
Hind Amoun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Fundus Images ◽  
Retinal Area ◽  
Hard Exudate ◽  
Retinal Blood Vessels

Abstract Classification of the stages of diabetic retinopathy (DR) is considered a key step in the assessment and management of diabetic retinopathy. Due to the damage caused by high blood sugar to the retinal blood vessels, different microscopic structures can be occupied in the retinal area, such as micro-aneurysms, hard exudate and neovascularization. The convolutional neural network (CNN) based on deep learning has become a promising method for the analysis of biomedical images. In this work, representative images of diabetic retinopathy (DR) are divided into five categories according to the professional knowledge of ophthalmologists. This article focuses on the use of convolutional neural networks to classify background images of DR according to disease severity and on the application of pooling, Softmax Activation to achieve greater accuracy. The aptos2019-blindness-detection database makes it possible to verify the performance of the proposed algorithm.

A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images Using Deep Learning Techniques

2021 ◽  
pp. 140-155
Author(s):  
Amiya Kumar Dash ◽  
Puspanjali Mohapatra
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Stage ◽  
Morphological Changes ◽  
Good Prognosis ◽  
Fundus Images ◽  
Fundus Image ◽  
Working Adults ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Diabetic retinopathy (DR) is a disease related to eye correlated with long-standing diabetes. It is a leading cause of blindness among working adults. Detection of this condition in the early stage is critical for good prognosis. Present day detection of DR normally requires digital fundus image or images generated using optical coherence tomography (OCT). As OCT are high-priced, diagnosis of DR using fundus image will benefit for the patient and the ophthalmologists. Manual inspection of morphological changes in blood vessels, microaneurysms, exudates, hemorrhages, and macula are time consuming and tedious tasks. So, designing a computer-aided system helps in analyzing the morphological changes and identifying the DR. This chapter reviews the applications of machine learning and deep learning algorithms for detection of nonproliferative diabetic retinopathy by analyzing fundus images.

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

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