Cerebral microbleed (CMB) is a type of biomarker, which is related to cerebrovascular diseases. In this paper, a novel computer aided diagnosis method for CMB detection was presented. Firstly, sliding neighborhood algorithm was used to generate CMB and non-CMB samples from brain susceptibility weighted images. Then, a 15-layer proposed FeatureNet was trained for extracting features from the input samples. Afterwards, structure after the first fully connected layer in FeatureNet was replaced by three randomized neural networks for classification: Schmidt neural network, random vector functional-link net, and extreme learning machine, and the weights and biases in early layers of FeatureNet were frozen during the training of those three classifiers. Finally, the output of the three classifiers was ensemble by majority voting mechanism to get better classification performance. In our experiment, five-fold cross validation was employed for evaluation. Results revealed that our FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, which outperformed several existing state-of-the-art approaches. The proposed FeatureNet-EN model could provide accurate CMB detection, and thus reduce death tolls.

Impact Statement — We propose a 15-layer FeatureNet to detect cerebral microbleed (CMB). We propose three FeatureNet variants: FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM. We use ensemble learning to combine three FeatureNet variants, and generate a FeatureNet-EN. The proposed FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, better than state-of-the-art methods. This method could provide accurate CMB detection, and thus reduce death tolls.

脑微出血（CMB）是一种与脑血管疾病相关的生物标志物。在本文中，提出了一种用于CMB检测的新型计算机辅助诊断方法。首先，使用滑动邻域算法从脑磁敏度加权图像中生成CMB和非CMB样本。然后，训练了一个 15 层的 FeatureNet，用于从输入样本中提取特征。之后，FeatureNet 中第一个全连接层之后的结构被三个随机神经网络代替进行分类：Schmidt 神经网络、随机向量功能链接网络和极限学习机，并且 FeatureNet 早期层的权重和偏差在此期间被冻结这三个分类器的训练。最后，三个分类器的输出通过多数投票机制集成以获得更好的分类性能。在我们的实验中，采用五重交叉验证进行评估。结果显示，我们的 FeatureNet-SNN、FeatureNet-RVFL 和 FeatureNet-ELM 的准确率分别为 98.22%、98.23% 和 97.54%，集成的 FeatureNet-EN 将准确率提高到 98.60%，优于现有的几种状态-最先进的方法。提出的 FeatureNet-EN 模型可以提供准确的 CMB 检测，从而减少死亡人数。并且集成的 FeatureNet-EN 将准确率提高到 98.60%，优于现有的几种最先进的方法。提出的 FeatureNet-EN 模型可以提供准确的 CMB 检测，从而减少死亡人数。并且集成的 FeatureNet-EN 将准确率提高到 98.60%，优于现有的几种最先进的方法。提出的 FeatureNet-EN 模型可以提供准确的 CMB 检测，从而减少死亡人数。

Impact Statement — 我们提出了一个 15 层的 FeatureNet 来检测脑微出血 (CMB)。我们提出了三种 FeatureNet 变体：FeatureNet-SNN、FeatureNet-RVFL 和 FeatureNet-ELM。我们使用集成学习来组合三个 FeatureNet 变体，并生成一个 FeatureNet-EN。提出的 FeatureNet-SNN、FeatureNet-RVFL 和 FeatureNet-ELM 的准确率分别为 98.22%、98.23% 和 97.54%，集成的 FeatureNet-EN 将准确率提高到 98.60%，优于最先进的技术方法。这种方法可以提供准确的CMB检测，从而减少死亡人数。