DMobileNet: A Novel MobileNet with Dendritic Learning for Brain Tumor Detection

Artificial intelligence advances quickly, making it possible to analyze brain images for tumor detection. Traditional computer vision techniques, while capable of identifying tumors, often lack the precision and adaptability required for accurate pre-diagnosis. When combined with deep learning, this improves the accuracy of tumor identification and pre-diagnosis by determining the position and size of the tumors. This advancement is helpful in guaranteeing that sufferers receive prompt treatment. Consequently, enhancing learning accuracy has emerged as a foundational necessity in the realm of medical image diagnosis. Inspired by dendritic neurons, researchers have devised the dendritic neuron model (DNM), which emulates the information processing characteristics of brain neurons within neural circuits. Combining this neuron with traditional deep learning models has become widely popular and consistently yields excellent results in solving classification problems. In this paper, we propose a novel architecture called DMobileNet, which by leveraging the strengths of MobileNets lightweight design and DNMs capability to emulate dendritic neuron behavior, achieves dynamic synaptic connections and enhanced information processing capabilities. Experimental results across various tasks demonstrated that DMobileNet outperformed both traditional MobileNet and established deep learning models in terms of accuracy and computational efficiency. In the case of the brain tumor problem, our model achieved an accuracy of 97.4% and an F1 score of 96.9 %. For classification tasks, this study proposes that utilizing DNM as a classifier may facilitate the advancement of more effective deep learning models.