Deep Learning for Weak Supervision of Diabetic Retinopathy Abnormalities

Ahmad, Maroof; Kasukurthi, Nikhil; Pande, Harshit

doi:10.1109/isbi.2019.8759417

Cited by 20 publications

(16 citation statements)

References 9 publications

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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%

“…Among them, the Diabetic Retinopathy Detection Dataset on Kaggle was the most preferred because of its size and accessibility. Besides, it is noteworthy that many nonpublic datasets were also used in most studies [23,44,45,47,54,57,55,38,52]. 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.…”

Section: Diabetic Retinopathy Datasetsmentioning

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%

“…[23], [44], [45], [47], [54], [57], [55], [38], [63], [52] Messidor Dataset 8 [36], [35], [63], [31], [60], [55], [39]…”

Section: Datasetmentioning

confidence: 99%

“…[59], [31], [32], [33], [56], [35], [36], [60], [39], [41], [47], [30], [50], [51], [64], [61], [53], [63], [54], [55], [24], [23], [46], [52] 3 [39], [45], [32] 4…”

Section: Number Of Classmentioning

confidence: 99%

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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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Section: Tl Strategy Studymentioning

confidence: 99%

Section: Diabetic Retinopathy Datasetsmentioning

confidence: 99%

Section: Diabetic Retinopathy Datasetsmentioning

confidence: 99%

“…[23], [44], [45], [47], [54], [57], [55], [38], [63], [52] Messidor Dataset 8 [36], [35], [63], [31], [60], [55], [39]…”

Section: Datasetmentioning

confidence: 99%

“…[59], [31], [32], [33], [56], [35], [36], [60], [39], [41], [47], [30], [50], [51], [64], [61], [53], [63], [54], [55], [24], [23], [46], [52] 3 [39], [45], [32] 4…”

Section: Number Of Classmentioning

confidence: 99%

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