Deep Learning-Based Diabetic Retinopathy Detection Using Fundus Images

Diabetic retinopathy (DR) is one of the most frequent causes of the blindness and vision loss in diabetic patients, which requires very early diagnosis and treatment. Traditional methods of DR detection require experienced ophthalmologists to visually inspect fundus images manually, which may be time consuming and subjective. In this research paper, automated deep learning fundus image-based diabetic retinopathy detection is presented. We propose a mix of convolutional neural network (CNN) and LSTM design to analyze retinal images of the eye and categorize the severity of DR into multiple phases, from moderate non-proliferative retinopathy to proliferative retinopathy. By using a large dataset of explained fundus images, our method learns to train CNN to identify complex patterns and features of DR. In order to fetch relevant information from fundus images, architecture employs numerous layers of convolutions, pooling, and activation functions. In order to enhance the model's performance, unified layers are mostly used for classification. Transfer learning and data augmentation techniques are employed to leverage the pre-trained networks that already exist. The following standard measures are used to assess the model's performance: accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).The deep learning model results shows that it outperforms the traditional methods in DR stage detection and classification with high accuracy and stable performance. The burden on healthcare facilities will reduce by using this machine learning technology which provides scalable option in early detection of DR. Vision loss can be prevented and initial treatment can be given to patients by introducing these technologies.