Abstract: This research paper presents a comprehensive eye disease detection system that aims to provide more accurate medical insights for diagnosing various eye conditions including Diabetic retinopathy, Cataract, Myopia, glaucoma, Age-related eye disorders, and eye disorders formed due to hyper Tension. The proposed system utilizes advanced image processing techniques and machine learning algorithms to analyze retinal images and extract key features that are indicative of different eye diseases. By incorporating more medical insights into the analysis process, the system enhances the accuracy of diagnosis and enables early detection of eye diseases, ultimately leading to timely treatment and prevention of potential complications. The system takes advantage of the large dataset of retinal images available, which have been annotated by expert ophthalmologists, allowing the machine learning algorithms to learn from a wide range of cases and make accurate predictions. Additionally, the system provides a user-friendly interface that allows ophthalmologists and other healthcare professionals to easily input patient data and obtain instant diagnostic results. The developed system has shown promising results in extensive experimental evaluations, with high accuracy rates achieved for the detection of all targeted eye diseases. In conclusion, this eye disease detection system represents a significant advance in the field of ophthalmology, offering a reliable and efficient tool for the early detection and management of a wide range of eye conditions.-diabetic retinopathy, cataract, myopia, glaucoma, age-related eye disorders, hypertension-induced eye disorder.

Keywords: Eye disease detection, Diabetic retinopathy, Cataract, Myopia, Glaucoma, Age-related eye disorders, Hypertension-induced eye disorders, Retinal images, Machine learning, Early detection.

Works Cited:

Varalakshmi. M, Iraniya Pandiyan. M, Kumaran. M " Deep Learning Advancements in Multispectral Eye Disease Detection: A Comprehensive Review ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 70-74, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12910

PDF | DOI: 10.17148/IJARCCE.2023.12910

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