Computer aided diagnosis systems are playing an important part in clinical treatment. They can help the doctors and physicians to verify the diagnosis decisions. In this study, a new classification algorithm for the brain magnetic resonance image is proposed. Initially, we utilized a MobileNetV2 to extract features from the input brain images, which was pre-trained on ImageNet dataset. Instead of training the deep network, we simply calculate the output of its certain layer to form the feature vector. The optimal feature layer is obtained by the experiment. Then, three different feedforward networks: extreme learning machine, Schmidt neural network and random vector functional-link net, are trained for classification. Chaotic bat algorithm was proposed to optimize the weights and biases in the three randomized neural networks to boost their classification accuracy. The result from 5×hold-out validation reveals that our method can achieve good generalization performance which is comparable to state-of-the-art pathological brain detection methods. The trained model can serve as a visual question answering system and produce accurate results.

计算机辅助诊断系统在临床治疗中发挥着重要作用。他们可以帮助医生验证医生的诊断决定。在这项研究中，提出了一种新的脑磁共振图像分类算法。最初，我们使用MobileNetV2从输入的大脑图像中提取特征，这些图像在ImageNet数据集上进行了预训练。无需训练深度网络，我们只需计算其特定层的输出即可形成特征向量。通过实验获得了最优特征层。然后，训练了三种不同的前馈网络：极限学习机，施密特神经网络和随机矢量功能链接网络进行分类。提出了混沌蝙蝠算法来优化三个随机神经网络的权重和偏差，以提高它们的分类精度。5倍保留验证的结果表明，我们的方法可以实现良好的泛化性能，可与最新的病理脑检测方法相媲美。训练后的模型可以用作可视问题解答系统并产生准确的结果。