Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning

Hathwar, Sagar B; Srinivasa, Gowri

doi:10.1109/icsipa45851.2019.8977760

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…The classification accuracy achieved by this method was 0.60. Hathwar and Srinivasa [5] explained a deep learning solution for automated grading of DR from retinal fundus images. They employed two Convolutional Neural Network (CNN) architectures Inception-ResNet-V2 [8,9] and Xception [10] that have achieved state-of-the-art performance in image recognition tasks.…”

Section: IImentioning

confidence: 99%

Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

Shaikh¹

2021

IJRASET

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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.

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Section: IImentioning

confidence: 99%

Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

Shaikh¹

2021

IJRASET

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“…Using pre-trained network without finetuning [30], [23] Fine-tuning entire pre-trained network [31], [32], [33], [34], [35], [36], [37], [38], [39], [22], [21], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [24], [53], [54], [55], [63] Fine-tuning a part of the pre-trained network [56], [34], [35] Training a state-of-art architecture from scratch [57], [34], [37], [40] Modifying a pre-trained network [58], [38], [42], [43], [45], [49], [55] Not stated [59], [60], [61]…”

Section: Tl Strategy Studymentioning

confidence: 99%

“…Deep neural networks work better on large datasets, and the size of the data set is a very important parameter in the network's performance. For this reason, most studies have used more than one dataset to improve classification performance [35,36,43,45,51,61,63,59,31,33,39,53,41].…”

Section: Diabetic Retinopathy Datasetsmentioning

confidence: 99%

“…Count Study Diabetic Retinopathy Detection Dataset on Kaggle 20 [36], [22], [21], [50], [51], [56], [37], [39], [58], [33], [31], [43], [49], [54], [63], [61], [64], [46], [59], [41] Non-public Dataset 10…”

Section: Datasetmentioning

confidence: 99%

“…The imbalance of the datasets and the quality of the images make it difficult to evaluate for more than two classes. For this reason, binary classification was used more than multi-class classification in the many reviewed studies [59,31,32,33,56,35,36,60,39,41,47,30,50,51,64,61,53,63,54,55,24].…”

Section: Datasets: the Number Of Classes Usedmentioning

confidence: 99%

See 2 more Smart Citations

A systematic review of transfer learning based approaches for diabetic retinopathy detection

Oltu¹,

Karaca²,

Erdem³

et al. 2021

Preprint

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Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 38 publications between 2015 and 2020. The published papers are summarized using 9 figures and 10 tables, giving information about 22 pre-trained CNN models, 12 DR data sets and standard performance metrics.

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DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only

Islam

Al-Absi

Ruagh³

et al. 2021

IEEE Access

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Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.INDEX TERMS Convolutional neural network, deep learning, diabetes, machine learning, Qatar, Qatar Biobank (QBB), retina.

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Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning

Cited by 8 publications

References 7 publications

Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

A systematic review of transfer learning based approaches for diabetic retinopathy detection

DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only

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