Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images
Article
Raghavendra, U., Pham, The-Hanh, Gudigar, Anjan, Vidhya, V., Rao, B. Nageswara, Sabut, Sukanta, Koh, Joel Koh En, Ciaccio, Edward J. and Acharya, U. Rajendra. 2021. "Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images." Complex and Intelligent Systems. 7 (2), pp. 929-940. https://doi.org/10.1007/s40747-020-00257-x
Article Title | Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images |
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ERA Journal ID | 212133 |
Article Category | Article |
Authors | Raghavendra, U., Pham, The-Hanh, Gudigar, Anjan, Vidhya, V., Rao, B. Nageswara, Sabut, Sukanta, Koh, Joel Koh En, Ciaccio, Edward J. and Acharya, U. Rajendra |
Journal Title | Complex and Intelligent Systems |
Journal Citation | 7 (2), pp. 929-940 |
Number of Pages | 12 |
Year | 2021 |
Publisher | SpringerOpen |
Place of Publication | Germany |
ISSN | 2198-6053 |
2199-4536 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s40747-020-00257-x |
Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107842377&doi=10.1007%2fs40747-020-00257-x&partnerID=40&md5=df5c95eab447b79965ff8d7f2cb25f9b |
Abstract | Brain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions. © 2021, The Author(s). |
Keywords | CAD; ICH index; Intracranial haemorrhage; Computed tomography; Probabilistic neural network |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Manipal Academy of Higher Education, India |
Ngee Ann Polytechnic, Singapore | |
Kalinga Institute of Industrial Technology, India | |
Columbia University, United States | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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