This article studies the problem of detecting cerebral micro-bleeds (CMBs) using a convolutional neural network (CNN). Cerebral micro-bleeds (CMBs) are increasingly recognized neuroimaging findings, occurring with cerebrovascular diseases, dementia, and normal aging. Naturally enough, it becomes necessary to detect CMBs in the early stages of life. The focus of this article is to infuse new techniques like Bayesian optimization to find the optimum set of hyper-parameters efficiently, making even the simplest of CNN architectures perform well on the problem. Experimentally, we observe our CNN (five layers, i.e., two convolution, two pooling, and one fully connected) achieves accuracy = 98.97%, sensitivity = 99.66%, specificity = 98.14%, and precision = 98.54% on the test set (hold-out validation) when calculated over an average of ten runs. The proposed model outperformed state-of-the-art methods.

中文翻译：

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使用CNN和贝叶斯优化来识别脑微出血

本文研究了使用卷积神经网络（CNN）检测脑微出血（CMB）的问题。大脑微出血（CMB）是越来越多的公认的神经影像学发现，发生于脑血管疾病，痴呆和正常衰老。自然地，有必要在生命的早期阶段检测CMB。本文的重点是注入诸如贝叶斯优化之类的新技术，以有效地找到最佳的超参数集，从而使最简单的CNN架构都能很好地解决该问题。在实验上，我们观察到我们的CNN（五层，即两个卷积，两个池化和一个完全连接）在测试集上保持精度= 98.97％，灵敏度= 99.66％，特异性= 98.14％和精度= 98.54％ -out验证），平均计算10次。