Ocular Disease Recognition using EfficientNet

Ocular diseases present a significant public health concern globally, warranting precise and efficient diagnostic methodologies. This research aims to develop an approach for identifying ocular diseases by classifying eight different eye conditions using fundus images. Leveraging transfer learning with EfficientNet, a powerful convolutional neural network architecture which is employed to learn discriminative features from the images, facilitating precise classification of various ocular diseases. Performance evaluations on the Ocular disease dataset ODIR highlight the practicality and effectiveness of our scheme. In addition to diagnostics, this combination has the potential for application in the fields of telemedicine and telehealth, enabling rapid and reliable diagnosis of eye diseases, especially in restricted or remote areas. The combination of Transfer Learning and CNN not only improves the accuracy of diagnosis but also simplifies the analysis process, helping to increase the efficiency of treatment and improve the patient’s outcome effect.