Dynamic Filter Application in Graph Convolutional Networks for Enhanced Spectral Feature Analysis and Class Discrimination in Medical Imaging

Graph Neural Networks (GNNs) offer a promising direction for medical image analysis, particularly due to their ability to capture complex relationships and handle non-Euclidean data structures often encountered in this domain. This study aims to demonstrate the potential of GNNs in medical image classification, showcasing their comparable performance to leading CNNs while using significantly fewer parameters. We introduce a novel GNN model, Graph Convolution Neural Network Enhanced Connectivity (GCNN-EC), incorporating an enhanced connectivity technique that leverages interrelationships between RGB channel features. Evaluated on the MedMNIST dataset, a standardized benchmark for medical image analysis, GCNN-EC performs competitively against pre-trained DNNs, highlighting its potential as an efficient and effective approach for medical image analysis. Importantly, we do not claim that GCNN-EC outperforms all CNNs in every scenario, but rather aim to showcase the potential of GNNs as a valuable tool in the medical imaging toolkit that can be used on its own or in conjuction with CNNs to further advance the state of the art. Additionally, our model outperforms traditional GNNs on standard image datasets like MNIST and CIFAR-10, mitigating the common issue of over-smoothing. These findings underscore the effectiveness of GCNs with dynamic filtering and encourage further research into GNN-based approaches for medical imaging and broader computer vision tasks.