Ocular Disease Detection Using Convolutional Neural Networks

Prior to the creation of the ophthalmoscope by von Helmholtz roughly 150 years ago, eye doctors had no way of inspecting the area behind the pupil. The recent decade has seen significant development in retinal imaging and image processing, opening up new frontiers in the study of the eye. Prevention of permanent vision loss due to ocular illnesses requires prompt diagnosis. In particular, convolutional neural networks (CNNs) have shown encouraging results in the analysis of medical pictures in recent years. This research provides support for the use of convolutional neural networks (CNNs) in the detection of ocular disorders. The proposed method takes retinal fundus images as input and employs a pre-trained convolutional neural network (CNN) model to extract relevant features. There are a number of benefits to using CNNs to forecast eye diseases instead of more conventional methods. Convolutional neural networks (CNNs) can learn complicated characteristics from big datasets, which can boost their accuracy and generalizability. Additionally, they are able to pick up on small changes in medical images that may be ignored by human experts, allowing for earlier diagnosis and treatment of ocular illnesses. Furthermore, CNNs can offer objective and reproducible predictions, which can help to lessen the variability and subjectivity of human evaluations. Convolutional neural networks (CNNs) are a sort of deep neural network that has shown effective for a number of computer vision tasks. Due to their propensity for learning complicated characteristics from big datasets, convolutional neural networks (CNNs) are ideally suited for the analysis of medical pictures like retinal fundus photographs and optical coherence tomography (OCT) images. For the purpose of ocular illness prediction, convolutional neural networks (CNNs) are trained using medical image annotation datasets to learn features that can distinguish between healthy and sick eyes. Then, new photos can be analyzed using these traits to determine the existence and severity of ocular disorders. The CNN model was trained and validated using over four thousand fundus images representing various ocular diseases and conditions. Eighty percent of the images were used for training, while the other twenty percent were used for testing. Training using two convolutional layers and two dense layers resulted in an 80% accuracy in predictions.