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Title: :  Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review
PaperId: :  20429
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20429
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Yashashree MahaleInternational Institute of Information Technology , Pune
Mahesh BandewarInternational Institute of Information Technology , Pune
Samyak SahooInternational Institute of Information Technology , Pune
Bhavesh JoshiInternational Institute of Information Technology , Pune
Prof.Sarang SaojiInternational Institute of Information Technology , Pune

Abstract

Computer Engineering
Diabetic retinopathy, Deep learning, fundus images, Image classification
Diabetic retinopathy (DR) is a prevalent and serious consequence of diabetes, affecting individuals worldwide, particularly in regions with limited access to technology and financial resources. The impact of DR on vision loss underscores the urgent need for early detection and intervention. DR is typically categorized into five stages, each indicative of varying degrees of retinal damage. The first stage is known as "no DR," denoting the absence of detectable damage to the retina. Following this stage, there are four progressive levels of severity: mild, moderate, severe, and proliferative DR. In this regard, artificial intelligence (AI) and deep learning technologies have emerged as invaluable tools in ophthalmology, offering automated solutions to complement traditional approaches. The integration of AI and deep learning into the diagnosis and monitoring of DR offers numerous benefits. These technologies can automate the screening process, allowing for early identification and timely intervention. By analyzing medical images, such as retinal scans, AI algorithms can detect subtle abnormalities and provide accurate assessments of disease progression. This not only enhances the efficiency of healthcare professionals but also ensures that patients receive appropriate treatment at the earliest possible stage. Artificial intelligence and deep learning techniques offer a transformative approach to automate the process, improving the accuracy, efficiency, and accessibility of DR diagnosis. By integrating these technologies into ophthalmology practices, we can make significant strides in reducing vision loss and improving the overall quality of care for individuals affected by diabetic retinopathy.

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IJARIIE Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. "Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 2104-2110
MLA Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. "Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 2104-2110.
APA Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, & Prof.Sarang Saoji. (2023). Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 2104-2110.
Chicago Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. "Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 2104-2110.
Oxford Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. 'Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 2104-2110. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Machine_Learning_and_Deep_learning_for_Diabetic_Retinopathy_Detection___A_Review_ijariie20429.pdf (Accessed : 27 May 2023).
Harvard Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. (2023) 'Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 2104-2110IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Machine_Learning_and_Deep_learning_for_Diabetic_Retinopathy_Detection___A_Review_ijariie20429.pdf (Accessed : 27 May 2023)
IEEE Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji, "Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 2104-2110, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Machine_Learning_and_Deep_learning_for_Diabetic_Retinopathy_Detection___A_Review_ijariie20429.pdf [Accessed : 27 May 2023].
Turabian Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. "Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 (27 May 2023).
Vancouver Yashashree Mahale, Mahesh Bandewar, Samyak Sahoo, Bhavesh Joshi, and Prof.Sarang Saoji. Machine Learning and Deep learning for Diabetic Retinopathy Detection: A Review. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 27 May 2023]; 9(3) : 2104-2110. Available from: https://ijariie.com/AdminUploadPdf/Machine_Learning_and_Deep_learning_for_Diabetic_Retinopathy_Detection___A_Review_ijariie20429.pdf
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