Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Abstract

Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. KEYWORDScerebral micro-bleeding, convolution neural network, detection, intra-cerebral hemorrhage, stochastic pooling, stroke INTRODUCTIONCerebral micro-bleedings (CMBs) are small chronic brain hemorrhages, which can be caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke and memory clinics that is initially set up for research purposes. 1 CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. 2 Previous studies show that, in a range of 18% to 68%, patients with ischemic stroke/transient ischemic attack have a high prevalence of CMBs. There are 23% of prevalence in patients with first-ever ischemic stroke and 44% in patients with recurrent ischemic stroke reported by a previous systematic review. 3 The strong relationship with CMBs and increased risk of ICH suggests that the antithrombotic drugs Concurrency Computat Pract Exper. 2020;32:e5130. wileyonlinelibrary.com/journal/cpe to test the performance of the proposed structure of CNN based on the stochastic pooling, we compared different pooling methods, including average pooling, max pooling, and stochastic pooling. Meanwhile, we compared CNN with different convolution number and the state-of-the-art method for CMB detection. The experiment result shows that our designed structure of CNN based on the stochastic pooling achieved the best performance in sensitivity as 97.22%, specificity as 97.35%, accuracy as 97.28%, and precision as 97.35%.The limitation of our work is that we may need to move the window from top to bottom and from left to right. This is time-consuming. It is better to use concurrent convolution in the future studies, and we may need to replace the fully connected layers with conv layers. Another limitation is that we have two labels (CMB and ...

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