Applied Intelligence and Computing
A Study: Performance of Convolutional Neural Networks in Diabetic Retinopathy Screening using Retinal Images
Authors: Dileep Kumar Agarwal and Maninder Singh Nehra
Publishing Date: 17-01-2025
ISBN: 978-81-955020-9-7
Abstract
Visualize technology and diagnostic strategies have come a good way in ophthalmology particularly with the use of machine understanding algorithms and artificial intelligence (AI). This evaluation article focuses on the advancement and use of numerous imaging methods like autofluorescence imaging, optical coherence tomography (OCT), retinal fundus digital photography in the diagnosis and therapy of retinal disorders. The investigation shows the improvement of image processing and the growing accuracy and reliability of computerized diagnostic techniques many of which can identify disorders like central serous chorioretinopathy, glaucoma, diabetic retinopathy with an accuracy rate of over 90%. Advancement of large datasets and AI’s capability to enhance ocular disease early medical diagnosis and treatment – and hence individual outcomes is also included in the study
Keywords
Artificial Intelligence, Clinical Validation, Diabetic Retinopathy, Ophthalmic Imaging
Cite as
Dileep Kumar Agarwal and Maninder Singh Nehra, "A Study: Performance of Convolutional Neural Networks in Diabetic Retinopathy Screening using Retinal Images", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2025, pp. 325-341. https://doi.org/10.56155/978-81-955020-9-7-31