A rapid increase in brain tumor cases mandates researchers for the automation of brain tumor detection and diagnosis. Multi-tumor brain image classification became a contemporary research task due to the diverse characteristics of tumors. Recently, deep neural networks are commonly used for medical image classification to assist neurologists. Vanishing gradient problem and overfitting are the demerits of the deep networks. In this paper, we have proposed a deep network model that uses ResNet-50 and global average pooling to resolve the vanishing gradient and overfitting problems. To evaluate the efficiency of the proposed model simulation has been carried out using a three-tumor brain magnetic resonance image dataset consisting of 3064 images. Key performance metrics have used to analyze the performance of the proposed model and its competitive models. We have achieved a mean accuracy of 97.08% and 97.48% with data augmentation and without data augmentation, respectively. Our proposed model outperforms existing models in classification accuracy.

脑肿瘤病例的迅速增加要求研究人员进行脑肿瘤检测和诊断的自动化。由于肿瘤的多种特征，多肿瘤脑图像分类成为当代研究任务。最近，深度神经网络通常用于医学图像分类，以协助神经科医生。消失的梯度问题和过度拟合是深层网络的缺点。在本文中，我们提出了一个深网络模型，该模型使用ResNet-50和全局平均池来解决消失的梯度和过度拟合问题。为了评估所提出模型的效率，已使用由3064张图像组成的三肿瘤脑磁共振图像数据集进行了仿真。关键性能指标已用于分析所提出模型及其竞争模型的性能。使用数据增强和不使用数据增强，我们的平均准确率分别为97.08％和97.48％。我们提出的模型在分类准确度方面优于现有模型。