Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

• Autorzy: Dogan Sengul , Barua Prabal Datta , Baygin Mehmet , Chakraborty Subrata , Ciaccio Edward J. , Tuncer Turker , Abd Kadir Khairul Azmi , Md Shah Mohammad Nazri , Azman Raja Rizal , Lee Chin Chew , Ng Kwan Hoong , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2022.06.004

• Języki publikacji: EN

Abstrakty

EN

This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi²-based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem. The P9 pooling function creates nine decomposed models, hence the name. The LPQ feature extractor is applied to images to generate sub-bands for feature generation. The Chi² selector is then employed to select the most significant features from the created feature vector, and these features are utilized for the classification using the k-nearest neighbor algorithm (kNN). The introduced P9-LPQ feature extraction-based learning model attained over 98% classification accuracy in all cases. The results obtained in this paper show that the proposed method can successfully classify stroke types. For this reason, the developed model can pre-diagnose stroke types in the future.

Słowa kluczowe

EN

LPQ operator; P9 pooling; brain image classification; computer vision; Chi2 selection; hand crafted features

PL

operator LPQ; obrazowanie mózgu; wizja komputerowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 815--828
• Opis fizyczny: Bibliogr. 51 poz., rys., tab., wykr.

Twórcy

autor

Dogan Sengul

• Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

autor

Barua Prabal Datta

• School of Management & Enterprise, University of Southern Queensland, Australia
• Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
• Cogninet Brain Team, Cogninet Australia, Sydney, Australia

autor

Baygin Mehmet

• Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey

autor

Chakraborty Subrata

• Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
• Faculty of Science, Agriculture, Business and Law, University of New England, Australia

autor

Ciaccio Edward J.

• Department of Medicine - Division of Cardiology, Columbia University, USA

autor

Tuncer Turker

• Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

autor

Abd Kadir Khairul Azmi

• Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

autor

Md Shah Mohammad Nazri

• Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

autor

Azman Raja Rizal

• Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

autor

Lee Chin Chew

• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

autor

Ng Kwan Hoong

• Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia

autor

Acharya U. Rajendra

• Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore
• Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
• Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

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