Diabetic Retinopathy Detection using Image Processing

,; Naveen, Dr.R.; Sivakumar, Dr.S.A.; ,; Shankar, Dr.B.Maruthi; ,; Priyaa, A.Keerthana; ,

doi:10.35940/ijeat.f1179.0886s19

Cited by 18 publications

(5 citation statements)

References 4 publications

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“…It proposed an automatic classification system, in which it analyzes fundus images (FunImg) with fluctuating illumination and fields of assessment and produces a severity grade for DR using ML replicas such as VGG-16, Convolutional Neural Network (CNN), and VGG-19 through five groups of classified images ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR. It accomplishes 82%, 80%, and 82% accuracy, sensitivity, and specificity, respectively [1]. Author Mushtaq et al proposed detection of DR using DL-based densely connected CNN (DenseNet-169) for identification of early recognition of DR, which categories the FunImgs based on their levels of severity: Proliferative-DR, Severe, Moderate, Mild, and No-DR with integration of DR-Recognition-2015 and Aptos-2019-Blindness-Recognition from Kaggle in the inclusion of datagathering, pre-processing, augmentation, and modeling levels and achieved 90% accuracy (ACU) [2].…”

Section: Related Workmentioning

confidence: 98%

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Automated Decision Making ResNet Feed-Forward Neural Network based Methodology for Diabetic Retinopathy Detection

Kumari¹,

Bhagat²,

Henge³

et al. 2023

IJACSA

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The detection of diabetic retinopathy eye disease is a time-consuming and labor-intensive process, that necessitates an ophthalmologist to investigate, assess digital color fundus photographic images of the retina, and discover DR by the existence of lesions linked with the vascular anomalies triggered by the disease. The integration of a single type of sequential image has fewer variations among them, which does not provide more feasibility and sufficient mapping scenarios. This research proposes an automated decision-making ResNet feed-forward neural network methodology approach. The mapping techniques integrated to analyze and map missing connections of retinal arterioles, microaneurysms, venules and dot points of the fovea, cottonwool spots, the macula, the outer line of optic disc computations, and hard exudates and hemorrhages among color and back white images. Missing computations are included in the sequence of vectors, which helps identify DR stages. A total of 5672 sequential and 7231 non-sequential color fundus and blackand-white retinal images were included in the test cases. The 80 and 20 percentage rations of best and poor-quality images were integrated in testing and training and implicated the 10-ford cross-validation technique. The accuracy, sensitivity, and specificity for testing and analysing good-quality images were 98.9%, 98.7%, and 98.3%, and poor-quality images were 94.9%, 93.6%, and 93.2%, respectively.

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Section: Related Workmentioning

confidence: 98%

“…Diabetic Retinopathy (DR) eye disease (ED) is correlated with chronic type diabetes, which is the primary trigger of sightlessness in children, workforce employees, and elderly people across the globe, and it is impacting more than 96 million people [1]. DR is a type of diabetes that causes damage to the retinal blood vessels (BV).…”

Section: Introductionmentioning

confidence: 99%

Automated Decision Making ResNet Feed-Forward Neural Network based Methodology for Diabetic Retinopathy Detection

Kumari¹,

Bhagat²,

Henge³

et al. 2023

IJACSA

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“…10 Exudates are yellow flicks that appear in the retina when blood from injured capillaries leaks due to the presence of lipid and protein residues. 11,12 The DR is often assessed according to various categories, such as severe NPDR, mild NPDR, PDR, moderate NPDR, and no DR. 13 A diagnosis of severe NDPR is made using the "4-2-1" rule. 14 One of the crucial elements in this is intraretinal microvascular abnormalities.…”

Section: Introductionmentioning

confidence: 99%

“…They resemble MAs if they are tiny 10 . Exudates are yellow flicks that appear in the retina when blood from injured capillaries leaks due to the presence of lipid and protein residues 11 , 12 . The DR is often assessed according to various categories, such as severe NPDR, mild NPDR, PDR, moderate NPDR, and no DR 13 .…”

Section: Introductionmentioning

confidence: 99%

Diabetic retinopathy classification using hybrid optimized deep-learning network model in fundus images

Bapatla,

Harikiran

2023

J. Electron. Imag.

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Many working-age people suffer from diabetic retinopathy (DR), one of the chronic retinal conditions caused by diabetes that eventually results in blindness. The classification of the DR severity level has been an arduous task due to the complexity of the lesion features. An efficient detection technique is needed for the screening procedure to categorize the retina's subtle pathologies. Deep neural networks are essential for identifying eye diseases and enabling ophthalmologists to treat patients immediately. To categorize the severity of DR utilizing images from the general IDRiD, MESSIDOR, and KAGGLE datasets, our research provides an effective hybrid optimized deep-learning network (HODLNet) model. The quality of the input fundus image is enhanced by performing preprocessing initially with noise removal and contrast enhancement. After that, to segment the blood vessels and optic disc, a modified ResUNet model is used. The features are then extracted after the lesion region has been found using the Gabor filter banks. An improved ShuffleNet v2 is used for the final classification process. According to the experimental results, the proposed HODLNet model produces higher results with a 99.84% accuracy for multiclass classification than the existing deep-learning models for MESSIDOR, IDRiD, and KAGGLE datasets.

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“…The retina of the eye has characteristics that are utilized to identify DR, including blood vessels, exudate, hemorrhage, microaneurysm, and texture. Image processing is required to provide greater understanding because these aspects are challenging to separate and spot faults in [19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

Toresa,

Wiza,

Anggraini

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus pictures to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created, and it is unknown whether one is best suited for use with retinal fundus images. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. This study investigated various image enhancement methods in order to see aberrant abnormalities on retinal fundus pictures more clearly. Contrast Limited Adaptive Histogram Equalization (CLAHE) method, Gray Level Slicing method, Median Filtering method, and Low Light method are image improvement methods used to enhance retinal fundus images. The parameters Natural Image Quality Evaluator (NIQE), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and entropy will be used to assess each image enhancement technique's performance. An ophthalmologist from Hospital University Sains Malaysia (HUSM) provided the image data. The findings indicate that while each technique has its own benefits, the CLAHE technique, with a standard deviation MSE of 0.0004, is the best.

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Diabetic Retinopathy Detection using Image Processing

Cited by 18 publications

References 4 publications

Automated Decision Making ResNet Feed-Forward Neural Network based Methodology for Diabetic Retinopathy Detection

Automated Decision Making ResNet Feed-Forward Neural Network based Methodology for Diabetic Retinopathy Detection

Diabetic retinopathy classification using hybrid optimized deep-learning network model in fundus images

Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening

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