Lu Siyuan, Liu Shuaiqi, Wang Shui-Hua, Zhang Yu-Dong
School of Informatics, University of Leicester, Leicester, United Kingdom.
College of Electronic and Information Engineering, Hebei University, Baoding, China.
Front Comput Neurosci. 2021 Sep 10;15:738885. doi: 10.3389/fncom.2021.738885. eCollection 2021.
Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment. In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs. Through the experiments, we found replacing the last five layers with ELM can get the optimal results. We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection.
脑微出血（CMBs）是分布于大脑的小圆形斑点，可导致中风、痴呆和死亡。早期诊断对治疗具有重要意义。本文针对脑磁共振图像提出了一种新的CMB检测方法。我们利用滑动窗口从输入的脑图像中获取训练和测试样本。然后，设计并训练了一个13层的卷积神经网络（CNN）。最后，我们提出利用极限学习机（ELM）替代CNN的最后几层进行检测。我们进行了一项实验来确定要替代的最佳层数。ELM中的参数通过一种名为蝙蝠算法的启发式算法进行优化。我们方法的评估基于留出验证，最终预测通过平均五次运行的性能生成。通过实验，我们发现用ELM替代最后五层可以获得最佳结果。我们与现有最先进算法进行了比较，结果表明我们的方法在CMB检测中是准确的。
