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Title: :  An investigation into the use of deep learning and image processing in the domain of diabetic medical care
PaperId: :  24894
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 4 2024
DUI:    16.0415/IJARIIE-24894
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
HR RavikumarShri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu, Rajasthan, India
Prasadu PeddiShri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu, Rajasthan, India

Abstract

CSE
Diabetic retinopathy condition may be better identified in the future by studying how several eye features, including the lens, macula, and retina, might be used for diagnosis. It guarantees the correct administration of medicine to prevent eye damage and aids in the early diagnosis of diseases that pose a danger to vision. A number of computer vision applications have begun to use deep learning due to its enormous popularity. When it comes to detecting diabetic disease and retinal MRI, these apps outperform humans. Machine learning can investigate previous events autonomously using supervised, unsupervised, and semi-supervised learning techniques. By obtaining better generalisation than fully connected layers, this model is able to do object identification by extracting very abstract features. Due to its efficient weight transfer between neurones, Convolutional Neural Networks (CNNs) are preferred over other models, allowing for a reduction in training parameters. During training, CNNs are able to avoid overfitting since they use a minimal number of parameters. The learning process cannot be completed without the classification and feature extraction steps. Using traditional models like Artificial Neural Networks, the problem of creating very precise forecasts for diabetic disease is addressed. A greater need for computer vision technology is being driven by advancements in core decision-making techniques, such as those in the medical, social, and other fields. Image processing allows object detection in computer vision frameworks by simulating visual experience. In this study, we build a convolutional neural network (CNN) that uses deep learning to divide diabetes images into five groups. Convolutional neural networks (CNNs) are tested on GPU-powered supercomputers.
Diabetic retinopathy condition may be better identified in the future by studying how several eye features, including the lens, macula, and retina, might be used for diagnosis. It guarantees the correct administration of medicine to prevent eye damage and aids in the early diagnosis of diseases that pose a danger to vision. A number of computer vision applications have begun to use deep learning due to its enormous popularity. When it comes to detecting diabetic disease and retinal MRI, these apps outperform humans. Machine learning can investigate previous events autonomously using supervised, unsupervised, and semi-supervised learning techniques. By obtaining better generalisation than fully connected layers, this model is able to do object identification by extracting very abstract features. Due to its efficient weight transfer between neurones, Convolutional Neural Networks (CNNs) are preferred over other models, allowing for a reduction in training parameters. During training, CNNs are able to avoid overfitting since they use a minimal number of parameters. The learning process cannot be completed without the classification and feature extraction steps. Using traditional models like Artificial Neural Networks, the problem of creating very precise forecasts for diabetic disease is addressed. A greater need for computer vision technology is being driven by advancements in core decision-making techniques, such as those in the medical, social, and other fields. Image processing allows object detection in computer vision frameworks by simulating visual experience. In this study, we build a convolutional neural network (CNN) that uses deep learning to divide diabetes images into five groups. Convolutional neural networks (CNNs) are tested on GPU-powered supercomputers.

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IJARIIE HR Ravikumar, and Prasadu Peddi. "An investigation into the use of deep learning and image processing in the domain of diabetic medical care" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 4 2024 Page 3377-3384
MLA HR Ravikumar, and Prasadu Peddi. "An investigation into the use of deep learning and image processing in the domain of diabetic medical care." International Journal Of Advance Research And Innovative Ideas In Education 10.4(2024) : 3377-3384.
APA HR Ravikumar, & Prasadu Peddi. (2024). An investigation into the use of deep learning and image processing in the domain of diabetic medical care. International Journal Of Advance Research And Innovative Ideas In Education, 10(4), 3377-3384.
Chicago HR Ravikumar, and Prasadu Peddi. "An investigation into the use of deep learning and image processing in the domain of diabetic medical care." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 4 (2024) : 3377-3384.
Oxford HR Ravikumar, and Prasadu Peddi. 'An investigation into the use of deep learning and image processing in the domain of diabetic medical care', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 4, 2024, p. 3377-3384. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/An_investigation_into_the_use_of_deep_learning_and_image_processing_in_the_domain_of_diabetic_medical_care_ijariie24894.pdf (Accessed : ).
Harvard HR Ravikumar, and Prasadu Peddi. (2024) 'An investigation into the use of deep learning and image processing in the domain of diabetic medical care', International Journal Of Advance Research And Innovative Ideas In Education, 10(4), pp. 3377-3384IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/An_investigation_into_the_use_of_deep_learning_and_image_processing_in_the_domain_of_diabetic_medical_care_ijariie24894.pdf (Accessed : )
IEEE HR Ravikumar, and Prasadu Peddi, "An investigation into the use of deep learning and image processing in the domain of diabetic medical care," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 4, pp. 3377-3384, Jul-Aug 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/An_investigation_into_the_use_of_deep_learning_and_image_processing_in_the_domain_of_diabetic_medical_care_ijariie24894.pdf [Accessed : ].
Turabian HR Ravikumar, and Prasadu Peddi. "An investigation into the use of deep learning and image processing in the domain of diabetic medical care." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 4 ().
Vancouver HR Ravikumar, and Prasadu Peddi. An investigation into the use of deep learning and image processing in the domain of diabetic medical care. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(4) : 3377-3384. Available from: https://ijariie.com/AdminUploadPdf/An_investigation_into_the_use_of_deep_learning_and_image_processing_in_the_domain_of_diabetic_medical_care_ijariie24894.pdf
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