Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection

Prudhvee Narasimha Goli; Pendela Harshadeep; Asapu Surendra; Muddineni krishna Vamsi

doi:10.36948/ijfmr.2024.v06i02.18176

Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection

Author(s)	Prudhvee Narasimha Goli, Pendela Harshadeep, Asapu Surendra, Muddineni krishna Vamsi
Country	India
Abstract	One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy. In order to preserve eyesight, diabetic retinopathy must be identified and treated early. In this study, "Vision Saver," a deep learning-based method for the automated detection and treatment of diabetic retinopathy, is introduced. Vision Saver provides a comprehensive solution for the detection of retinal problems by utilizing deep neural networks, enabling prompt patient care and intervention. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two cutting-edge deep learning approaches, are used by the proposed system to evaluate retinal images and provide precise diagnoses. Additionally, we investigate how generative adversarial networks (GANs) might improve image quality and produce artificial data for training. The architecture of Vision Saver is built to handle both fundus photos and optical coherence tomography (OCT) images, enabling a flexible method of evaluating diabetic retinopathy. Vision Saver's management component includes not only detection but also monitoring of therapy progression. The system aids medical personnel in making educated judgments about interventions like laser therapy, injections, or surgical operations through ongoing monitoring and follow-up. The development and assessment of Vision Saver are covered in this essay, with a focus on how well it performs in terms of sensitivity, specificity, and accuracy. We also discuss concerns with model interpretability, data protection, and integration into clinical workflows. Ultimately, the objective is to improve patient outcomes and lessen the burden of blindness brought on by diabetic retinopathy by offering a comprehensive solution that improves screening and management of the illness. Deep learning in healthcare has a lot of potential, and Vision Saver is an example of how it can significantly improve the lives of people with diabetic retinopathy.
Keywords	Diabetic Retinopathy, Deep Learning, Detection, Management, Vision Saver, Healthcare Technology
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-23
Cite This	Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection - Prudhvee Narasimha Goli, Pendela Harshadeep, Asapu Surendra, Muddineni krishna Vamsi - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18176
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.18176
Short DOI	https://doi.org/gtrssg

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Vision Saver Deep Learning Approaches for Diabetic Retinopathy Detection

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