Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 1 , PP: 01-22, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nima Khodadadia 1 * , Benyamin Abdollahzadeha 2
Doi: https://doi.org/10.54216/JAIM.100101
Retinal diseases lead to the loss of vision and are a significant burden to health, and a timely and accurate diagnosis should be conducted to maximize treatment and clinical outcome. The research has been applied in the holistic examination of various eye health diseases such as cataracts, glaucoma and retinary aberrations which are separated into normal eye related cases and artificial networks. Using a large set of retinal images, the study conducts a thorough quantitative analysis of both complicated models like CNN, K-NN, and SVM in the form of parameters of accuracy, sensitivity, specificity, and F-Score. The CNN model had a better performance with a fantastic overall accuracy 94.05% and good sensitivity in classifying pathological states. It can be proven by the comparative analysis that CNN architecture is an effectual diagnostic instrument in the sphere of ophthalmology and demonstrates tremendous prospects in the replication of ophthalmology screening screening with the help of ophthalmology automation. This timely and vast assessment of the machine learning methods contributes a lot to the literature not only in terms of establishing relative lines between different technological solutions but also in helping style the advanced technological solutions to carry out screening to help the ophthalmologist make reliable diagnostic prescriptions.
Retinal diseases , Machine learning , Convolutional neural networks , Eye disease classification , Diagnostic accuracy , Ophthalmology
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