The most frequent brain tumor types are gliomas. The magnetic resonance imaging technique helps to make the diagnosis of brain tumors. It is hard to get the diagnosis in the early stages of the glioma brain tumor, although the specialist has a lot of experience. Therefore, for the magnetic resonance imaging interpretation, a reliable and efficient system is required which helps the doctor to make the diagnosis in early stages. To make classification of the images, to which class the glioma belongs, convolutional neural networks, which proved that they can obtain an excellent performance in the image classification tasks, can be used. Convolutional network hyperparameters’ tuning is a very important issue in this domain for achieving high accuracy on the image classification; however, this task takes a lot of computational time. Approaching this issue, in this manuscript, we propose a metaheuristics method to automatically find the near-optimal values of convolutional neural network hyperparameters based on a modified firefly algorithm and develop a system for automatic image classification of glioma brain tumor grades from magnetic resonance imaging. First, we have tested the proposed modified algorithm on the set of standard unconstrained benchmark functions and the performance is compared to the original algorithm and other modified variants. Upon verifying the efficiency of the proposed approach in general, it is applied for hyperparameters’ optimization of the convolutional neural network. The IXI dataset and the cancer imaging archive with more collections of data are used for evaluation purposes, and additionally, the method is evaluated on the axial brain tumor images. The obtained experimental results and comparative analysis with other state-of-the-art algorithms tested under the same conditions show the robustness and efficiency of the proposed method.

最常见的脑肿瘤类型是神经胶质瘤。磁共振成像技术有助于做出脑部肿瘤的诊断。尽管专家有很多经验，但很难在神经胶质瘤脑肿瘤的早期阶段进行诊断。因此，对于磁共振成像解释，需要一种可靠且有效的系统，该系统可帮助医生尽早进行诊断。为了对神经胶质瘤所属的图像进行分类，可以使用卷积神经网络，该网络证明了它们在图像分类任务中可以获得出色的性能。为了实现图像分类的高精度，卷积网络超参数的调整是该领域非常重要的问题。但是，此任务需要大量的计算时间。为了解决这个问题，在本文中，我们提出了一种元启发式方法，该方法基于改进的萤火虫算法自动找到卷积神经网络超参数的接近最佳值，并开发了一种通过磁共振成像对神经胶质瘤脑肿瘤等级进行自动图像分类的系统。首先，我们在标准无约束基准函数集上测试了所提出的改进算法，并将性能与原始算法和其他改进变量进行了比较。在总体上验证了该方法的有效性后，将其用于卷积神经网络的超参数优化。IXI数据集和具有更多数据集的癌症影像档案可用于评估目的，此外，该方法在轴向脑肿瘤图像上进行评估。获得的实验结果以及在相同条件下测试的其他最新算法的对比分析显示了该方法的鲁棒性和效率。