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@article{179797,
        author = {Vaishnavi Shitole and Yash Kalwar and Nandini Patil and Raghunandan Rajesh Somani and Dr. S. R. Ganorkar},
        title = {Diabetic Retinopathy Detection using Image Processing and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7999-8004},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179797},
        abstract = {Diabetic retinopathy remains one of the most 
severe complications of diabetes, highlighting the need 
for efficient healthcare systems capable of early 
diagnosis and treatment. This project intends to create a 
prototype for an automated detection and diagnosis 
system for diabetic retinopathy using sophisticated 
machine learning and image processing technologies. 
The system autonomously analyzes retinal images to 
detect and classify the various stages of diabetic 
retinopathy, 
thus 
replacing 
the 
traditional 
methodologies with greater accuracy and efficiency. The 
work consists of acquiring and preprocessing datasets of 
retinal images, performing feature extraction, and 
applying diverse machine learning techniques to train 
and validate the model. The system will be integrated 
into a simple, user-oriented app, allowing healthcare 
professionals to remote conduct reliable and prompt 
screenings in rural or deprived regions. In addition, the 
integration with the Electronic Health Records (EHR) 
system aims to streamline patient data management and 
monitoring. The system will undergo thorough testing 
with real-world data to confirm reliability and 
robustness of performance. Furthermore, this project 
seeks to improve the accuracy and efficiency of 
diagnostics and educate patients on the necessity of 
screening for diabetic retinopathy, ultimately improving 
patient outcomes while alleviating strain on the 
healthcare systems.},
        keywords = {},
        month = {May},
        }