Leveraging the Capabilities of EfficientNetB3 to Classify Eye Diseases with Reliability

The objective of this research is to examine the utilization of deep learning in the classification of ocular diseases and healthy lives, with a specific emphasis on the EfficientNetB3 architecture. By employing a convolutional neural network (CNN), it is possible to differentiate among various eye conditions, such as diabetic retinopathy, cataracts, and normal vision. Based on the ImageNet dataset pre-training of the EfficientNetB3 model, which functions as the foundation for feature extraction, custom layers are incorporated to refine and classify the model. The evaluation of the proposed model's performance is conducted on distinct test and validation sets. In addition to the recall and confusion matrix, other pertinent metrics such as precision and recall are utilized to evaluate the model's capability of accurately categorizing every eye disease. The findings offer valuable insights regarding the efficacy of the EfficientNetB3 architecture in classifying ocular diseases and its potential practical implementation in clinical settings. The results have the potential to facilitate the creation of dependable and effective instruments for timely identification and intervention in the field of ophthalmic healthcare.