Eye Disease Classification Enhanced by EfficientNetB3: A Deep Learning Approach

Deep learning and computer vision have especially improved medical diagnostics in ophthalmology and other fields. Eye diseases include cataracts, diabetic retinopathy, and glaucoma, and normal problems offer major challenges considering their frequency and impact on vision health. Conventional diagnosis methods rely on the subj ective and prone-to-error manual examination that ophthalmologists provide. This work investigates the use of a state-of-the-art deep learning architecture, Fine Tuned EfficientN etB3, for the automated categorization of various eye illnesses. We use a large collection of 3,424 high-quality retinal pictures taken from several repositories and annotated by professionals. We raise the quality of the dataset by using rigorous preprocessing and augmentation techniques. Fine-tuned via transfer learning, the EfficientN etB3 model shows great performance with a 94% general accuracy. Evaluations of precision, recall and F l-score show the consistency of the model across several disease diagnoses. Particularly in underdeveloped areas, the model's exceptional accuracy and efficiency emphasize its capacity to improve clinical operations, raise diagnosis accuracy, and give access to professional eye care. Our efforts provide scalable solutions for early and accurate eye disease diagnosis, therefore advancing the expanding field of artificial intelligence-driven medical diagnostics.