Diabetic Retinopathy Detection using Deep Learning Methodology

Kumar, Arsha V; Babu, A. Suresh

doi:10.1109/gcat55367.2022.9971891

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…With this design, DR detection has an accuracy of 0.9611 (quadratic weighted kappa score of 0.8981). And ultimately, they are contrasting the VGG16 and the two CNN architectures accuracy values are 0.7326 and 0.9611 for architecture and DenseNet121 architecture, respectively [24].…”

Section: S H Kassani Et Al's Modifiedmentioning

confidence: 98%

Enhanced model to detection of Diabetic Retinopathy using Deep Learning techniques

Abdel Hussein Abdel Amir,

Majeed Hilal Almiahi

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Diagnosing and treating diabetic retinopathy (DR) early on can prevent vision loss. None, moderate, mild, proliferate, and severe are the top five DR phases. This work presents a deep learning (DL) model that identifies all five stages of DR more accurately than earlier approaches. the suggested method 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 circular mask with using random search for hyperparameter. The next step was using augmentation techniques to create a balanced dataset using the same parameters for both scenarios. The created model outperformed previous techniques for identifying the five stages of DR, with an accuracy of 90% , using Efficient net B3 applied to the Asia Pacific TeleOphthalmology Society (APTOS) datasets.

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Section: S H Kassani Et Al's Modifiedmentioning

confidence: 98%

Enhanced model to detection of Diabetic Retinopathy using Deep Learning techniques

Abdel Hussein Abdel Amir,

Majeed Hilal Almiahi

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…Jones and Patel (2019) introduced a unique deep learning system that incorporates advanced image processing techniques for feature extraction in DR detection, adding new approaches to the area [4]. Furthermore, Kumar and Gupta (2020) studied the integration of transfer learning with deep learning models for DR detection, demonstrating the possibility for using pre-trained models to improve classification accuracy [5]. Finally, Brown et al ( 2021) investigated ensemble learning strategies in DR detection, demonstrating how integrating multiple classifiers can increase classification performance [6].…”

Section: Literature Surveymentioning

confidence: 99%

Quantum driven deep learning for enhanced diabetic retinopathy detection

M. Mounika,

L. Sahithi,

K. Prasanna Lakshmi

et al. 2024

World J. Adv. Res. Rev.

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Traditional convolutional neural networks (CNNs) have shown potential for recognizing retinopathy caused by diabetes (DR). However, developing quantum computing has the possibility for improved feature representation. We propose a hybrid approach that combines classical CNNs with quantum circuits to capitalize on both classical and quantum information for DR classification. Using the Keras and Qiskit frameworks, our model encodes picture features into quantum states, allowing for richer representations. Through experiments on a collection of retinal pictures, our model displays competitive performance, with excellent reliability and precision in categorizing DR severity levels. This combination of classical and quantum paradigms offers a fresh approach to enhancing DR diagnosis and therapy.

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