Brain tumors classification based on segmentation techniques and wavelet transform

Document Type : Research Paper

Authors

Department of Computer Science, University of Baghdad, Baghdad, Iraq

Abstract

This paper aims to provide better approaches for segmenting and classifying brain tumours using Magnetic Resonance images (MRI). In this paper, the wavelet features are formed by the transformation of probability density function (PDF) to spectrogram images using Continuous Wavelet Transform (2D-CWT), which is a simple method for extracting features, whereas the Feature extraction methods (PDF and 2D-CWT) are improving the performance. In addition, a morphological operation for segmenting images and a convolutional neural network (CNN) are utilized as a classifier in order to increase the segmentation performance. On the BraTS2019 dataset, the method's performance is assessed in terms of F1-score and tumor region segmentation accuracy. This achieved the greatest results, with accuracy and F1-score of 97.37 \% and 97.43 \%, respectively.

Keywords

[1] G.K. Aksahin, Brain tumor prediction on MR images with semantic segmentation by using deep learning network
and 3D imaging of tumor region, Biomed. Signal Process. Control. 66 (2021), 102458.
[2] M. Arbane, R. Benlamri, Y. Brik and M. Djerioui, Transfer learning for automatic brain tumor classification
using MRI images, Int. Workshop Human-Centric Smart Envir. Health Well-being (IHSH), 2020.
[3] S.P. Archa, C. Sathish Kumar, Segmentation of brain tumor in MRI images using CNN with Edge detection, Int.
Conf. Emerg. Trends Innov. Engin. Technol. Res. (ICETIETR), 2018.
[4] BraTS2019, MICCAI’s Dataset on Brain Tumor Segmentation, Kaggle, 2019.
[5] D. Chudasama, T. Patel and Sh. Joshi, Image segmentation using morphological operations, Int. J. Comput. Appl.
117 (2015), no. 18.
[6] S. Deepak and P. M. Ameer, Automated categorization of brain tumor from MRI using CNN features and SVM,
J. Ambient Intell. Humanized Comput. 12 (2021), no 8, 8357–8369.
[7] A. Gurunathan and B. Krishnan, Detection and diagnosis of brain tumors using deep learning convolutional neural
networks, Int. J. Imag. Syst. Technol. 31 (2021), no. 3, 1174–1184.[8] C. Jaspin Jeba Sheel and G. Suganthi, Morphological edge detection and brain tumor segmentation in Magnetic
Resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM)
algorithm, Multimedia Tools Appl. 79 (2020), no. 25, 17483–17496.
[9] F. Javier D´ıaz-Pernas, M. Mart´ınez-Zarzuela, M. Ant´on-Rodr´ıguez and D. Gonz´alez-Ortega, A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network, Healthcare
9 (2021), no. 2, 153.
[10] B. Kokila, M.S. Devadharshini, A. Anitha and S. Abisheak Sankar, Brain tumor detection and classification using
deep learning techniques based on MRI images, J. Phys.: Conf. Ser. 1916 (2021), no. 1, 12226.
[11] J.M. Lilly and S.C. Olhede, Generalized morse wavelets as a superfamily of analytic wavelets, IEEE Trans. Signal
Process. 60 (2012), no. 11, 6036–6041.
[12] Ch. Lodh Choudhury, Ch. Mahanty and R. Kumar, Brain tumor detection and classification using convolutional
neural network and deep neural network, Int. Conf. Comput. Sci. Engin. Appl., IEEE, 2020.
[13] M. Mohammed Thaha, K. Pradeep Mohan Kumar, B.S. Murugan, S. Dhanasekeran, P. Vijayakarthick and A.
Senthil Selvi, Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Medical
Imag. 35 (2016), no. 5, 1240–1251.
[14] M.A. Naser and M. Jamal Deen, Brain tumor segmentation and grading of lower-grade glioma using deep learning
in MRI images, Comput. Bio. Medicine 121 (2020), 103758.
[15] Sh. Nemaa, A. Dudhane, S. Muralaa and S. Naidu, RescueNet: An unpaired GAN for brain tumor segmentation,
Biomed. Signal Process. Control 55 (2020), 101641.
[16] S.V. Sasank and S. Venkateswarlu, An automatic tumour growth prediction based segmentation using full resolution
convolutional network for brain tumour, Biomed. Signal Process. Control 71 (2022), 103090.
[17] H. Sourabh and J. Chandra, Convolutional neural network for brain tumor analysis using MRI images, Int. J.
Engin. Technol. 11 (2019), 67–77.
[18] R. Thillaikkarasi and S. Saravanan, An enhancement of deep learning algorithm for brain tumor segmentation
using kernel based CNN with M-SVM, J. Medical Syst. 43 (2019), no. 4, 1–7.
Volume 13, Issue 2
July 2022
Pages 2247-2256
  • Receive Date: 03 February 2022
  • Revise Date: 20 April 2022
  • Accept Date: 18 May 2022