Identification of Diabetic Retinopathy from fundus images using CNNs

Math*, Laxmi; Fatima, Ruksar

doi:10.35940/ijitee.a4598.119119

Cited by 5 publications

(11 citation statements)

References 8 publications

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“…Resize/Reshape/Rescale [59], [56], [34], [35], [58], [37], [60], [21], [41], [42], [43], [44], [30], [50], [51], [64], [24], [61], [54], [63], [49], [23], [32], [45], [32], [53], [31], [36], [46] Normalization [59], [33], [36], [37], [38], [39], [22], [42], [49], [64], [55], [61] Crop [31], [33], [34], [35], [58] [39], [48], [30], [50], [55], [63],…”

Section: Pre-processing Methods Studymentioning

confidence: 99%

“…Not given [59], [31], [57], [37], [60], [21], [43], [23], [46], [47], [48], [30], [50], [51], [24], [61], [53] Rotation [32], [56], [34], [35], [36], [58], [38], [39], [22], [40], [41], [44], [45], [49], [55], [54], [64], [52] Flipping [56], [35], [36], [58], [38], [22], [41], [44], [45], [49], [63], [54], [64], [52] Translation [32], …”

Section: Data Augmentation Methods Studymentioning

confidence: 99%

“…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%

“…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%

“…[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: Pre-processing Methods Studymentioning

confidence: 99%

Section: Data Augmentation Methods Studymentioning

confidence: 99%

Section: Tl Strategy Studymentioning

confidence: 99%

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%

See 3 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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show abstract

Retinal blood vessel segmentation using pixel-based feature vector

Toptaş

Hanbay

2021

Biomedical Signal Processing and Control

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Enhancing Ocular Healthcare: Deep Learning-Based Multi-Class Diabetic Eye Disease Segmentation and Classification

Vadduri,

Kuppusamy

2023

IEEE Access

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Diabetic Eye Disease (DED) is a serious retinal illness that affects diabetics. The timely identification and precise categorization of multi-class DED within retinal fundus images play a pivotal role in mitigating the risk of vision loss. The development of an effective diagnostic model using retinal fundus images relies significantly on both the quality and quantity of the images. This study proposes a comprehensive approach to enhance and segment retinal fundus images, followed by multi-class classification employing pre-trained and customized Deep Convolutional Neural Network (DCNN) models. The raw retinal fundus dataset was subjected to experimentation using four pre-trained models: ResNet50, VGG-16, Xception, and EfficientNetB7, and the optimal performing model EfficientNetB7 was acquired. Then, image enhancement approaches including the green channel extraction, applying Contrast-Limited Adaptive Histogram Equalization (CLAHE), and illumination correction, were employed on these raw images. Subsequently, image segmentation methods such as the Tyler Coye Algorithm, Otsu thresholding, and Circular Hough Transform are employed to extract essential Region of Interest (ROIs) like optic nerve, Blood Vessels (BV), and the macular region from the raw ocular fundus images. After preprocessing, the model is trained using these images that outperformed the four pre-trained models and the proposed customized DCNN model. The proposed DCNN methodology holds promising results for the Cataract (CA), Diabetic Retinopathy (DR), Glaucoma (GL), and NORMAL detection tasks, achieving accuracies of 96.43%, 98.33%, 97%, and 96%, respectively. The experimental evaluations highlighted the efficacy of the proposed approach in achieving accurate and reliable multi-class DED classification results, showcasing the promising potential for early diagnosis and personalized treatment. This contribution could lead to improved healthcare outcomes for diabetic patients.

show abstract

Identification of Diabetic Retinopathy from fundus images using CNNs

Cited by 5 publications

References 8 publications

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

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

Retinal blood vessel segmentation using pixel-based feature vector

Enhancing Ocular Healthcare: Deep Learning-Based Multi-Class Diabetic Eye Disease Segmentation and Classification

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