Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling. (6th January 2019)

• Record Type: Journal Article
• Authors: Wang, Shuihua; Sun, Junding; Mehmood, Irfan; Pan, Chichun; Chen, Yi; Zhang, Yu‐Dong
• Other Names: Sangaiah Arun Kumar guestEditor.; Pham Hoang guestEditor.; Qiu Tie guestEditor.; Muhammad Khan guestEditor.; Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Hussain Farookh guestEditor.
• Is Part Of: Concurrency and computation. Volume 32:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-06
• Subjects: cerebral micro‐bleeding -- convolution neural network -- detection -- intra‐cerebral hemorrhage -- stochastic pooling -- stroke; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5130
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.