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. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

中文翻译：

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基于树突状结构的肿瘤检测深度学习

脑肿瘤检测通常涉及对各种肿瘤类型进行分类。基于 McCulloch-Pitts 模型的传统分类器因其结构过于简单且检测具有复杂特征的脑肿瘤图像的能力有限而受到批评。在这项研究中，我们提出了一种受神经元树突结构启发的多分类模型，该模型利用突触和树突非线性信息处理能力。使用脑肿瘤检测数据集的实验结果表明，我们提出的模型在所有评估指标上都优于其他最先进的模型。 © 2024 日本电气工程师协会和 Wiley periodicals LLC。