Aim: 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. Method: 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. Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results. Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection.

中文翻译：

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通过卷积神经网络和极限学习机进行脑微出血检测。

目的：脑微出血 (CMB) 是分布在大脑上的小圆点，可导致中风、痴呆和死亡。早期诊断对治疗具有重要意义。方法：本文提出了一种新的脑磁共振图像CMB检测方法。我们利用滑动窗口从输入的大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，我们建议使用极限学习机 (ELM) 来代替 CNN 中的最后几层进行检测。我们进行了一项实验，以确定要替换的最佳层数。ELM 中的参数通过启发式算法 bat 算法进行优化。对我们方法的评估基于保留验证，最终预测是通过平均五次运行的性能产生的。结果：通过实验，我们发现用 ELM 替换最后五层可以获得最佳结果。结论：我们提供了与最先进算法的比较，可以看出我们的方法在 CMB 检测中是准确的。