A Dendritic Architecture-Based Deep Learning for Tumor Detection

Brain tumor detection typically involves classifying various tumor types. Traditional classifiers, based on the McCulloch-Pitts model, have faced criticism due to their oversimplified structure and limited capabilities in detecting brain tumor images with complex features. In this study, we propose a multiclassification model inspired by dendritic architectures in neurons, which leverages synaptic and dendritic nonlinear information processing capabilities. Experimental results using brain tumor detection datasets demonstrate that our proposed model outperforms other state-of-the-art models across all evaluation metrics.