Convolutional Neural Network for Multi-class Classification of Diabetic Eye Disease

Prompt examination increases the chances of effective treatment of Diabetic Eye Disease (DED) and reduces the likelihood of permanent deterioration of vision. A key tool commonly used for the initial diagnosis of patients with DED or other eye disorders is the screening of retinal fundus images. Manual detection with these images is, however, labour intensive and time consuming. As deep learning (DL) has recently been demonstrated to provide impressive benefits to clinical practice, researchers have attempted to use DL method to detect retinal eye diseases from retinal fundus photographs. DL techniques in machine learning (ML) have achieved state-of-the-art performance in the binary classification of healthy and diseased retinal fundus images while the classification of multi-class retinal eye diseases remains an open challenge. Multi-class DED is therefore considered in this study seeking to develop an automated classification framework for DED. Detecting multiple DEDs from retinal fundus images is an important research topic with practical consequences. Our proposed model was tested on various retinal fundus images gathered from the publicly available dataset and annotated by an ophthalmologist. This experiment was conducted employing a new convolutional neural network (CNN) model. Our proposed model for multi-class classification achieved a maximum accuracy of 81.33%, sensitivity of 100%, and specificity of 100%.