Optimized dynamic global structure enhanced multi-channel graph neural network based automatic cataract disease classification

Abstract

The most prevalent condition that causes vision distortion is cataracts. The greatest method to reduce the danger and prevent blindness is to detect cataracts accurately and timely detection. Research interest in the cataract detection systems based on artificial intelligence is recently increased. In this manuscript, Optimized Dynamic Global Structure Enhanced Multi-channel Graph Neural Network depend Automatic Cataract Disease Classification (DSEMGNN-CACD-SETOA) is proposed. The input fundus images are obtained using glaucoma dataset and the image is given to pre-processing. The input images are pre-preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMMCKF) to resize and normalize the image. The pre-processed imagery is provided to the categorization. Finally, the pre-processed imageries are provided to Dynamic Global Structure Enhanced multi-channel Graph Neural Network (DSEMGNN) to classify cataract disease as Referable Glaucoma and Non-referable Glaucoma. The Stock Enhancing Trading Optimization Algorithm (SETOA) is proposed for improving the weight parameter of DSEMGNN for cataract disease classification. The proposed KOAC-GCIGNN-AcME-SBOA technique is implemented on Python. The proposed approach attains 28%, 30.78% and 25.29% higher accuracy, 15.08%, 20.58%, and 15.25% higher precision when comparing with the existing methods like GLA-Net: A global–local attention network for automated cataract categorization (GLAN-CNN-ACC), cataract grading technique depending on deep convolutional neural networks and stacking ensemble learning (CGM-DCNN-SEL),automated cataract detection scheme with deep learning for fundus imageries (ACD-DNN-FI)respectively.