Deep Learning In Ophthalmology: Predicting Eye Diseases Using Pre-trained Neural Network

• Author(s):

  Nazreen Riazudeen S | Jayasundhar V.K | Sureendrababu R | Vickram S

• Keywords:

  Deep Learning, Glaucoma Detection, Ophthalmology, Xception CNN

• Abstract:

  This Study Presents A Deep Learning Approach For Predicting Multiple Retinal Diseases Using Fundus Images. Leveraging A Pre-trained Xception CNN Model Optimized For Multi-label Classification, The System Accurately Detects Conditions Such As Diabetic Retinopathy, Glaucoma, Cataract, And Age-related Macular Degeneration. Preprocessing Techniques Like Normalization And Contrast Enhancement Are Applied To Improve Diagnostic Performance. Trained On Annotated Datasets And Evaluated Using Clinical Metrics, The Model Demonstrates High Accuracy And Potential For Real-world Integration. This AI-driven Tool Aims To Assist Ophthalmologists In Early Diagnosis And Extend Quality Eye Care To Remote And Under-resourced Areas.

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Other Details

• Paper id:

  IJSARTV11I5103636

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-22

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