Cerebral Micro-Bleed Detection Based on the Convolution Neural Network With Rank Based Average Pooling | Zendy

Shuihua Wang | Zendy; Yongyan Jiang | Zendy; Xiaoxia Hou | Zendy; Hong Cheng | Zendy; Sidan Du | Zendy

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Cerebral Micro-Bleed Detection Based on the Convolution Neural Network With Rank Based Average Pooling

Author(s) -

Shuihua Wang,

Yongyan Jiang,

Xiaoxia Hou,

Hong Cheng,

Sidan Du

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2736558

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Cerebral micro-bleed (CMB) is small perivascular hemosiderin deposits from leakage through cerebral small vessels. They can result from cerebra-vascular disease, dementia, or simply from normal aging. It can be visualized via the susceptibility weighted imaging (SWI). Based on the SWI, we propose to use different structures of the CNN with rank-based average pooling to detect the CMB, and compare this method used in this paper to the current state-of-the-art methods. We can find that the CNN with five layers obtains the best performance, with a sensitivity of 96.94%, a specificity of 97.18%, and an accuracy of 97.18%.

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