Dogan, Sengul and Barua, Prabal Datta and Baygin, Mehmet and Chakraborty, Subrata and Ciaccio, Edward J. and Tuncer, Turker and Abd Kadir, Khairul Azmi and Shah, Mohammad Nazri Md and Azman, Raja Rizal and Lee, Chin Chew and Ng, Kwan Hoong and Acharya, U. Rajendra (2022) Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts. Biocybernetics and Biomedical Engineering, 42 (3). pp. 815-828. ISSN 0208-5216, DOI https://doi.org/10.1016/j.bbe.2022.06.004.
Full text not available from this repository.Abstract
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 Chi(2)-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 d
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | LPQ; P9 pooling; Brain image classification; Computer vision; Chi2 selection; Hand-crafted features |
| Subjects: | R Medicine > R Medicine (General) R Medicine > R Medicine (General) > Medical technology |
| Divisions: | Faculty of Medicine Faculty of Medicine > Biomedical Imaging Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Nov 2024 01:43 |
| Last Modified: | 09 Nov 2024 01:43 |
| URI: | http://eprints.um.edu.my/id/eprint/41879 |
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