Title: Machine learning approach for automatic brain tumour detection using patch-based feature extraction and classification

Authors: T. Kalaiselvi; P. Kumarashankar; P. Sriramakrishnan

Addresses: Department of Computer Science and Applications, School of Computer Science and Technologies, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, 624302, India ' Department of Computer Science and Applications, School of Computer Science and Technologies, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, 624302, India ' Department of Computer Applications, School of Computing, Kalasalingam Academy of Research and Education (Deemed to be University), Krishnankoil, Tamil Nadu, 626126, India

Abstract: Manual selection of tumorous slices from MRI volume is a time expensive process. In the proposed work, we have developed an automatic method for tumorous slice classification from MRI head volume. The proposed method is named as patch-based classification (PBC). PBC uses 8 × 8 patch-based feature extraction and SVM-based classification. Optimal features selection has been done by infinite feature selection (IFS) method. The optimal features are extracted from patches and used to train the SVM classifier to construct the tumour prediction model (TPM). TPM helps to identify the tumorous patches in the testing phase. Accuracy is measured using sensitivity, specificity and accuracy, error rates are calculated using false alarm (FA) and missed alarm (MA). Training and testing patches are chosen from whole brain atlas (WBA) and BraTS datasets. Results of PBC are compared with several existing methods. PBC will be more helpful to physicians who currently undergo a tedious and time consuming process of manually selecting the tumorous slices from the MRI volumes.

Keywords: tumour detection; feature extraction; feature blocks; brain tumour; BraTS dataset.

DOI: 10.1504/IJBET.2022.124665

International Journal of Biomedical Engineering and Technology, 2022 Vol.39 No.4, pp.396 - 411

Received: 25 May 2019
Accepted: 26 Aug 2019
Published online: 05 Aug 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article