Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems

Hacisoftaoglu, Recep E.; Karakaya, Mahmut; Sallam, Ahmed B.

doi:10.1016/j.patrec.2020.04.009

Cited by 96 publications

(55 citation statements)

References 18 publications

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“…A few specialists centre around finding and proposing few procedures or strategies for distinguishing certain highlights of diabetic retinopathy (i.e., microaneurysms, haemorrhages, exudates, and Neovascularisation). Some scientists proposed the improvement of robotized frameworks for distinguishing and ordering typical or strange diabetic retinopathy [31].…”

Section: Related Workmentioning

confidence: 99%

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Zia¹,

Irum²,

Qadri³

et al. 2022

Computers, Materials &Amp; Continua

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Diabetes or Diabetes Mellitus (DM) is the upset that happens due to high glucose level within the body. With the passage of time, this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy (DR) which can cause a major loss of vision. The symptoms typically originate within the retinal space square in the form of enlarged veins, liquid dribble, exudates, haemorrhages and small scale aneurysms. In current therapeutic science, pictures are the key device for an exact finding of patients' illness. Meanwhile, an assessment of new medicinal symbolisms stays complex. Recently, Computer Vision (CV) with deep neural networks can train models with high accuracy. The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction (DR). The proposed deep learning framework utilizes the strength of selected models (VGG and Inception V3) by fusing the extracated features. To select the most discriminant features from a pool of features, an entropy concept is employed before the classification step. The deep learning models are fit for measuring the highlights as veins, liquid dribble, exudates, haemorrhages and miniaturized scale aneurysms into various classes. The model will ascertain the loads, which give the seriousness level of the patient's eye. The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures.

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Section: Related Workmentioning

confidence: 99%

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Zia¹,

Irum²,

Qadri³

et al. 2022

Computers, Materials &Amp; Continua

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“…In deep learning method, network directly learns low level depiction and high level parameters from data directly and lowers the need of human intervention for feature engineering. Hacisoftaoglu et al [12] presented a method based on GoogleNet, AlexNet, ResNet50 and CNN based framework to increase the result of DR recognition in cell-phone based and conventional fundus camera retina pictures. Retraining of these frameworks are performed on various datasets such as Messidor, EyePACS, IDRiD to examine the outcome of deploying images from different group of single, multiple, cross datasets.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

A Transfer Learning Approach for Diabetic Retinopathy and Diabetic Macular Edema Severity Grading

Nasir¹,

Afreen²,

Patel³

et al. 2021

RIA

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Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are complication that occurs in diabetic patient especially among working age group that leads to vision impairment problem and sometimes even permanent blindness. Early detection is very much needed for diagnosis and to reduce blindness or deterioration. The diagnosis phase of DR consumes more time, effort and cost when manually performed by ophthalmologists and more chances of misdiagnosis still there. Research community is working on to design computer aided diagnosis system for prior detection and for DR grading based on its severity. Ongoing researches in Artificial Intelligence (AI) have set out the advancement of deep learning technique which comes as a best technique to perform analysis and classification of medical images. In this paper, research is applied on Resnet50 model for classification of DR and DME based on its severity grading on public benchmark dataset. Transfer learning approach accomplishes the best outcome on Indian Diabetic Retinopathy Image Dataset (IDRiD).

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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%

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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Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems

Cited by 96 publications

References 18 publications

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

A Transfer Learning Approach for Diabetic Retinopathy and Diabetic Macular Edema Severity Grading

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

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