Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

Oliver Faust, Joel En Wei Koh, Vicnesh Jahmunah, Sukant Sabut, Edward J. Ciaccio, Arshad Majid, Ali Ali, Gregory Y. H. Lip and U. Rajendra Acharya
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Oliver Faust: Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
Joel En Wei Koh: School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
Vicnesh Jahmunah: School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
Sukant Sabut: School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha 751024, India
Edward J. Ciaccio: Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA
Arshad Majid: Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK
Ali Ali: Sheffield Teaching Hospitals NIHR Biomedical Research Centre, Sheffield S10 2JF, UK
Gregory Y. H. Lip: Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool L69 7TX, UK
U. Rajendra Acharya: School of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore

IJERPH, 2021, vol. 18, issue 15, 1-19

Abstract: This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p -value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.

Keywords: stroke type classification; Magnetic Resonance Imaging; Support Vector Machine; adaptive symmetric sampling; Higher Order Spectra (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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