International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Diabetic Retinopathy Detection using CNN and Telemedicine

Author(s) Kunjan Prashant Desale, Sanika Sandeep Jadhav, Chaitali Bharat More, Shrushti Umesh Shirbhate, Pallavi V. Bhaskare, N. S. Kokate
Country India
Abstract Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early detection and timely intervention are critical for preventing severe complications. However, the scarcity of ophthalmologists, especially in rural and underdeveloped regions, limits the accessibility of timely diagnosis and treatment. Telemedicine, combined with advanced artificial intelligence (AI) techniques, such as Convolutional Neural Networks (CNN), offers a promising solution for improving DR detection and patient care remotely. This paper presents a comprehensive survey of recent advancements in DR detection using CNN-based deep learning models, along with telemedicine frameworks for real-time consultation and treatment suggestions. We discuss the underlying architecture of CNN models and their role in automating retinal image classification.
Keywords Diabetic retinopathy detection, Semantic segmentation, Convolutional neural network(CNN), Color fundus images , Telemedicine.
Field Computer
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-25
Cite This Diabetic Retinopathy Detection using CNN and Telemedicine - Kunjan Prashant Desale, Sanika Sandeep Jadhav, Chaitali Bharat More, Shrushti Umesh Shirbhate, Pallavi V. Bhaskare, N. S. Kokate - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29306
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.29306
Short DOI https://doi.org/g8pnmg
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