Dendritic Aggregated Residual Deep Learning for Meningioma MRI Diagnosis

The diagnosis of meningioma through magnetic resonance imaging (MRI) holds significant importance in clinical medicine. To enhance the accuracy of meningioma diagnosis, a more effective image classification method is required to comprehensively capture subtle features within MRI images. Although deep learning networks have been successfully applied to this problem, the conventional neural networks based on McCulloch-Pitts neurons suffer from low performance and insufficient feature extraction, due to simplistic structure and neglect of the nonlinear effects of synapses. Therefore, we propose a novel dendritic learning-based ResNeXt model, named DResNeXt. It utilizes the residual structure of ResNeXt and the cardinality method to adequately extract features of MRI images. Then, we innovationally introduce a dendritic neural model to improve the nonlinear information processing of biological neurons for comprehensively handling extracted features. Experimental results demonstrate the outstanding performance of the proposed DResNeXt model in the classification task of meningioma MRI dataset, surpassing the ResNeXt model in preventing overfitting. Additionally, compared to other deep learning models, it exhibits higher accuracy and superior image classification performance.