Abstract : Our novel system, Enhancing Vision Care: Detection of Eye Diseases and Prediction of Refractive Errors, employs a deep learning architecture trained on a dataset of diverse fundus photographs encompassing various eye diseases, including diabetic retinopathy, glaucoma and cataracts and prediction of refractive errors like myopia, astigmatism and hypermetropia. The system employs multi-task learning and attention mechanisms to simultaneously detect and localize distinct disease signatures within each image. This project represents a significant step towards automated, multi-disease eye disease detection with high accuracy and generalizability. Its potential lies in enabling early intervention, improving individual prognosis, and reducing healthcare costs associated with vision loss. Future work will focus on integrating Enhancing Vision Care: Detection of Eye Diseases and Prediction of Refractive Errors into clinical workflows and exploring its application in underserved communities.
Keywords: Artificial intelligence, deep learning, multi-disease detection, eye diseases, retinal imaging, early diagnosis, healthcare
Cite:
Ananya, Manojna P Jain, Nidah Shabbir Shaikh, Vinayashree, Dr.Sreeja Rajesh,"Enhancing Vision Care: Detection of Eye Diseases and Prediction of Refractive Errors", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.133120.
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DOI:
10.17148/IJARCCE.2024.133120
