Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.

脑肿瘤是世界上最致命的癌症之一。它是成人和儿童的常见癌症。它的生存率最低，根据其位置，纹理和形状的不同，类型也不同。肿瘤脑的错误分类将导致不良后果。因此，在早期阶段确定正确的肿瘤类型和等级对于选择精确的治疗计划具有重要作用。检查患者大脑的磁共振成像（MRI）图像代表了一种区分脑部肿瘤的有效技术。由于大量的数据和各种脑瘤类型，手动技术变得很耗时，并可能导致人为错误。因此，需要一个自动化的计算机辅助诊断（CAD）系统。图像分类技术的最新发展显示出巨大的进步，特别是在该领域成功的深度卷积神经网络（CNN）。在这方面，我们利用CNN解决脑肿瘤分类的问题。我们提出了一个新模型，该模型包含多个层次，目的是对MRI脑肿瘤进行分类。所提出的模型在三个数据集上进行了实验评估。实验结果证实，与现有方法相比，该方法具有令人信服的性能。所提出的模型在三个数据集上进行了实验评估。实验结果证实，与现有方法相比，该方法具有令人信服的性能。所提出的模型在三个数据集上进行了实验评估。实验结果证实，与现有方法相比，该方法具有令人信服的性能。