A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network

Fatima,; Imran, Muhammad; Ullah, Anayat; Arif, Muhammad; Noor, Rida

doi:10.1016/j.compbiomed.2022.105424

Cited by 24 publications

(10 citation statements)

References 38 publications

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“…It integrated 35,126 images from EyePACS and achieved 97.08% ACU. The authors, Fatima, Muhammad Imran, et al [49], proposed a unified method for entropy improvement-based DR recognition using a hybrid NN. It devised manipulating the discrete wavelet transforms to enhance the visibility of medical imaging by making the delicate features more prominent, and it classified images for further stages.…”

Section: Related Workmentioning

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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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: 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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show abstract

“…After extracting textural characteristics with local binary patterns, Adriman [12] reported a performance assessment of DR systems utilizing several deep learning techniques. In order to increase the accessibility of the medical pictures, Fatima [13] suggested a complete approach based on a hybrid neural network for recognizing DR. Gayathri [17] utilized a multipath CNN and machine learning classifiers to develop an automatic DR detection system. Bodapati [18] built a composite DNN using a gated-attention mechanism for automatic DR severity classification.…”

Section: Related Workmentioning

confidence: 99%

Detection of Retinopathy Diseases Using Convolutional Neural Network Based on Discrete Cosine Transform

KABBOURI,

OKATAN

2021

EURAS

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This master thesis proposes a new approach to detecting retinopathy diseases using a convolutional neural network (CNN) based on discrete cosine transform (DCT). Retinopathy is a common eye disease that can cause vision loss if not diagnosed and treated early. The proposed method combines the power of CNN and DCT to improve the accuracy of detection. The input image is transformed into the frequency domain using DCT, which reduces the amount of noise and emphasizes the important features of the image. Then, the transformed image is fed into the CNN for classification. The performance of the proposed method is evaluated using a publicly available dataset of retinal images. The results show that the proposed method outperforms existing methods in terms of accuracy and computational efficiency. The proposed method has the potential to be used in real-world applications for early diagnosis and treatment of retinopathy diseases.

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“…This may cause gradual vision impairment and, in its most severe form, blindness. It is not possible to cure the illness, so treatment focuses on preserving the patient’s present level of eyesight [ 6 , 7 ]. In most cases, a patient’s sight may be saved if DR is diagnosed and treated as soon as possible.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

2023

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Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model’s performance and learning ability.

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A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network

Cited by 24 publications

References 38 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

Detection of Retinopathy Diseases Using Convolutional Neural Network Based on Discrete Cosine Transform

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

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