Joint DR-DME grading classification using optimal feature selection-based deep graph correlation network

Abstract

Diabetic macular edema (DME) and diabetic retinopathy (DR) are the leading causes of human blindness, and accurate grading of individual DME, individual DR, and the joint DR-DME is very important for the diagnosis of human eye diseases. However, the conventional methods failed to separate the features such as disease-specific, disease-dependent, and joint DR-DME, which resulted in poor grading accuracy. In addition, optimal feature selection is also vital in DR-DME grading classification for improving the performance of joint DR-DME grading classification. Therefore, this work focuses on the implementation of an advanced deep graph correlation learning model based on a joint DR-DME network (JDD-Net) for disease detection and classification from color fundus images. Initially, convolutional block attention module (CBAM) and joint disease attention (JDA) modules are combined to extract the DR-specific, DME-specific, and joint DR-DME disease-dependent features. Here, interdependent DR and DME features are separated by a CBAM-based channel-spatial split attention mechanism. In addition, an iterative random forest network (IRF-Net) is used to select the optimal features by adopting fast machine learning properties. Finally, a deep graph correlation network (DGCN) is used to classify the different diseases using a pre-trained model. The simulations conducted on the Indian Diabetic Retinopathy Image Dataset (IDRiD) disclose that the proposed JDD-Net results in improved individual DR, individual DME, and joint DR-DME performance as compared to state-of-the-art approaches with DR, DME, and joint DR-DME accuracy of 99.53%, 99.1%, and 99.01%, respectively.