Optimizing Ocular Disease Detection: A Deep Learning Approach with VGG19 and InceptionV3

Early detection and treatment are especially important as global eye diseases including diabetic retinopathy, glaucoma, age-related macular degeneration, and cataracts create major public health issues. Ophthalmology has changed as recent advancements in Deep Learning (DL) and medical imaging allow accurate and fast ailment diagnosis. This work investigates fundus images-based automated categorization of ocular diseases using VGG19 architecture and InceptionV3. Combining two DL approaches InceptionV3 transfer learning model, well-known for their performance in image recognition applications, and VGG19, a deep neural network noted for depth and accuracy in visual classification—the approach followed InceptionV3 showed an 89.44% accuracy after being trained and evaluated using a 5,000 patient record Ocular Disease intelligence recognition (ODIR) dataset including color fundus images and diagnostic keywords; VGG19 shown a higher accuracy of 94.95%, so implying its improved performance in disease categorization. VGG19 proves to outperform InceptionV3 in accuracy, and loss by being able to generalize over numerous patient demographics and imaging modalities. Most importantly for early intervention and improved patient outcomes in clinical settings, our comparison analysis underlines VGG19's accuracy and resilience in automated illness diagnosis.