Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.

目前，脑肿瘤的诊断和分类仍依赖于活检标本的组织病理学分析。当前的方法是侵入性的，费时的并且容易发生人为错误。这些缺点表明，基于深度学习对大脑肿瘤进行多分类的全自动方法非常重要。本文旨在使用卷积神经网络（CNN）对脑肿瘤进行多分类以用于早期诊断。针对三种不同的分类任务，提出了三种不同的CNN模型。使用第一个CNN模型，可以以99.33％的准确度实现脑肿瘤检测。第二个CNN模型可以将脑肿瘤分为正常，神经胶质瘤，脑膜瘤，垂体和转移性五种脑瘤类型，准确度为92.66％。第三个CNN模型可以将脑肿瘤分为II级，III级和IV级三个等级，准确度为98.14％。使用网格搜索优化算法自动指定CNN模型的所有重要超参数。据作者所知，这是第一项使用CNN对脑肿瘤MRI图像进行多分类的研究，其几乎所有超参数均由网格搜索优化器调整。将拟议的CNN模型与其他流行的最新CNN模型进行比较，例如AlexNet，Inceptionv3，ResNet-50，VGG-16和GoogleNet。使用大型且可公开获得的临床数据集可获得令人满意的分类结果。