Automated detection of multi-class brain abnormalities through magnetic resonance imaging (MRI) has received much attention due to its clinical significance and therefore has become an active area of research over the years. The earlier automated schemes often followed traditional machine learning paradigms, in which the proper choice of features and classifiers has remained a major concern. Therefore, deep learning algorithms have been profoundly applied in various medical imaging applications. In this paper, a deep convolutional neural network (CNN) based automated approach is designed for the diagnosis of multi-class brain abnormalities. The proposed CNN model comprises five layers with learnable parameters: four convolutional layers and one fully-connected layer. The objective of designing such a custom deep network is to achieve greater classification performance with reduced number of parameters. The proposed model is evaluated on two benchmark multi-class brain MRI datasets namely, MD-1 and MD-2. The model achieved a classification accuracy of 100.00% and 97.50% on MD-1 and MD-2 datasets respectively. Moreover, four pre-trained CNN models based on the transfer learning approach have been tested over the same datasets. The comparative analysis with existing schemes indicates the superiority of the proposed method.

由于其临床意义，通过磁共振成像（MRI）自动检测多类脑部异常已引起广泛关注，因此，近年来已成为研究的活跃领域。早期的自动化方案通常遵循传统的机器学习范式，其中正确选择功能和分类器仍然是一个主要问题。因此，深度学习算法已广泛应用于各种医学成像应用中。本文设计了一种基于深度卷积神经网络（CNN）的自动化方法来诊断多类脑部异常。所提出的CNN模型包括五个具有可学习参数的层：四个卷积层和一个完全连接层。设计这样的定制深度网络的目的是在减少参数数量的情况下实现更高的分类性能。在两个基准的多类别脑MRI数据集MD-1和MD-2上评估了提出的模型。该模型在MD-1和MD-2数据集上的分类精度分别为100.00％和97.50％。此外，已经在相同的数据集上测试了基于转移学习方法的四个预训练的CNN模型。与现有方案的比较分析表明了该方法的优越性。在相同的数据集上测试了基于转移学习方法的四个预训练的CNN模型。与现有方案的比较分析表明了该方法的优越性。在相同的数据集上测试了基于转移学习方法的四个预训练的CNN模型。与现有方案的比较分析表明了该方法的优越性。