Classification of 3D-DWT Features of Brain Tumours with SVM

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Mucahid Barstugan


Brain tumours are one of the most challenging medical conditions to diagnose and treat. Accurate and timely classification of brain tumours is critical for effective treatment planning and patient management. Machine learning algorithms have shown great promise in improving the accuracy of brain tumour classification. This study implemented high-grade glioma (HGG) and low-grade glioma (LGG) classification on four different 3D-MRI (magnetic resonance imaging) scans (FLAIR, T1, T1c, T2). By using four different scans, 15 different combinations were created for classification process. 3D Discrete Wavelet Transform was used to transform tumour images for feature extraction stage. 36 different wavelet types were used for image transformation. First Order Statistics (mean, variance, kurtosis, skewness, entropy, energy) were extracted from transformed images of 36 wavelet types. Support Vector Machines (SVM) algorithm classified the FOS features that were obtained on BraTS 2017 dataset. The 2-fold, 5-fold, and 10-fold cross-validations are implemented and six metrics (sensitivity, specificity, accuracy, precision, F1-score, AUC) evaluated the performance of proposed method. Consequently, proposed method achieved remarkable scores of 95.23% (sensitivity), 78.81% (specificity), 90.89% (accuracy), 92.59% (precision), 93.89% (F1-score), and 87.02% (AUC) for HGG/LGG classification of 3D brain MRI data on T1+T1c+T2 combination by 2-fold cross validation.

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Barstugan, M. (2023). Classification of 3D-DWT Features of Brain Tumours with SVM. Orclever Proceedings of Research and Development, 2(1), 39–49.


H. Li, A. Li, M. J. C. i. b. Wang, and medicine, "A novel end-to-end brain tumor segmentation method using improved fully convolutional networks," vol. 108, pp. 150-160, 2019. DOI:

Y. Li, F. Jia, and J. J. A. i. i. m. Qin, "Brain tumor segmentation from multimodal magnetic resonance images via sparse representation," vol. 73, pp. 1-13, 2016. DOI:

M. Soltaninejad et al., "Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI," vol. 12, pp. 183-203, 2017. DOI:

B. J. J. o. M. Ural and B. Engineering, "A computer-based brain tumor detection approach with advanced image processing and probabilistic neural network methods," vol. 38, pp. 867-879, 2018. DOI:

H. Koyuncu, M. Barstuğan, M. Ü. J. M. Öziç, B. Engineering, and Computing, "A comprehensive study of brain tumour discrimination using phase combinations, feature rankings, and hybridised classifiers," vol. 58, pp. 2971-2987, 2020. DOI:

J. Amin, M. Sharif, N. Gul, M. Yasmin, and S. A. J. P. R. L. Shad, "Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network," vol. 129, pp. 115-122, 2020. DOI:

J. Amin, M. Sharif, M. Raza, T. Saba, and A. Rehman, "Brain tumor classification: feature fusion," in 2019 international conference on computer and information sciences (ICCIS), 2019, pp. 1-6: IEEE. DOI:

S. Kuraparthi et al., "Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network," vol. 38, no. 4, 2021. DOI:

M. I. Sharif, J. P. Li, M. A. Khan, and M. A. J. P. R. L. Saleem, "Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images," vol. 129, pp. 181-189, 2020. DOI:

B. H. Menze et al., "The multimodal brain tumor image segmentation benchmark (BRATS)," vol. 34, no. 10, pp. 1993-2024, 2014.

S. Bakas et al., "Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features," vol. 4, no. 1, pp. 1-13, 2017. DOI:

S. Bakas et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation," vol. 10, 2018.

N. Aggarwal, B. Rana, R. J. I. J. o. I. S. Agrawal, and Technology, "3d discrete wavelet transform for computer aided diagnosis of A lzheimer's disease using t1‐weighted brain MRI," vol. 25, no. 2, pp. 179-190, 2015. DOI:

Z. Chen, R. J. C. M. I. Ning, and Graphics, "Breast volume denoising and noise characterization by 3D wavelet transform," vol. 28, no. 5, pp. 235-246, 2004. DOI:

C. Yücelbas, S. Yucelbas, S. Ozsen, G. Tezel, S. Kuccukturk, and S. J. I. J. S. T. Yosunkaya, "Detection of sleep spindles in sleep EEG by using the PSD methods," vol. 9, no. 25, pp. 1-7, 2016. DOI:

C. Yücelbaş et al., "Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods," vol. 29, pp. 17-33, 2018. DOI:

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, B. J. I. I. S. Scholkopf, and t. applications, "Support vector machines," vol. 13, no. 4, pp. 18-28, 1998. DOI: