Brain Tumor Classification using Framelet Transform based energy features and K-Nearest Neighbor classifier

Authors

  • Amjath Ali J
  • TEEBA ABDULLAH SAID ALJAHDHAMI MIDDLE EAST COLLEGE
  • Intissar Nasser Abdullah Al-Habsi
  • Al-Salt Yaqoob Ali Al-Suti

Keywords:

MRI brain images, Framelet Transform, Energy features, KNN classifier

Abstract

The tissues which are abnormal in the brain are known as brain tumor. The growth of tumor creates the more pressure in the function of the brain and cause headache, sickness and other problems. The early diagnosis is required for the brain tumor. In this study, a technique for brain tumor classification using framelet transform based energy features and K-Nearest Neighbor (KNN) classifier is presented.  The normal and abnormal Magnetic Resonance Images (MRI) brain images are fed into framelet transform and the features are decomposed into subband coefficients. These framelet based decomposed features are extracted by energy features. These extracted features are given as input for KNN classifier. Results show the better classification accuracy of MRI brain classification images using framelet transform based energy features and KNN classifier.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References or Bibliography

Ma, C., Luo, G. and Wang, K., 2018. Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE transactions on medical imaging, 37(8), pp.1943-1954.

Gumaei, A., Hassan, M.M., Hassan, M.R., Alelaiwi, A. and Fortino, G., 2019. A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor Classification. IEEE Access.

Wu, G., Chen, Y., Wang, Y., Yu, J., Lv, X., Ju, X., Shi, Z., Chen, L. and Chen, Z., 2017. Sparse representation-based Radiomics for the diagnosis of brain tumors. IEEE transactions on medical imaging, 37(4), pp.893-905.

Kaur, T., Saini, B.S. and Gupta, S., 2017. Quantitative metric for MR brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation. IET Image Processing, 11(8), pp.620-632.

Kermi, A., Andjouh, K. and Zidane, F., 2018. Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets. IET Image Processing, 12(11), pp.1964-1971.

Patel, D., Vankawala, F. and Bhatt, B., 2019, April. A Survey on Identification of Glioblastoma Multiforme and Low-Grade Glioma Brain Tumor Type. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0335-0339). IEEE.

Shahzadi, I., Tang, T.B., Meriadeau, F. and Quyyum, A., 2018, December. CNN-LSTM: Cascaded Framework For Brain Tumour Classification. In 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) (pp. 633-637). IEEE.

Krishnammal, P.M. and Raja, S.S., 2019, April. Convolutional Neural Network based Image Classification and Detection of Abnormalities in MRI Brain Images. In 2019 International Conference on Communication and Signal Processing (ICCSP) (pp. 0548-0553). IEEE.

Afshar, P., Plataniotis, K.N. and Mohammadi, A., 2019, April. Capsule Networks for Brain Tumor Classification Based on Mri Images and Coarse Tumor Boundaries. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1368-1372). IEEE.

Shekhar, S. and Ansari, M.A., 2018, April. Image Analysis for Brain Tumor Detection from MRI Images using Wavelet Transform. In 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC) (pp. 670-675). IEEE.

Jemimma, T.A. and Raj, Y.J.V., 2018, June. Brain Tumor Segmentation and Classification Using Deep Belief Network. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1390-1394). IEEE.

R. Ezhilarasi and P. Varalakshmi, "Tumor Detection in the Brain using Faster R-CNN," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 388-392.

Kebir, S.T. and Mekaoui, S., 2018, November. An Efficient Methodology of Brain Abnormalities Detection using CNN Deep Learning Network. In 2018 International Conference on Applied Smart Systems (ICASS) (pp. 1-5). IEEE.

Dubey, Y.K., Mushrif, M.M. and Pisar, K., 2018, December. Brain Tumor Type Detection Using Texture Features in MR Images. In 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-4). IEEE.

Published

06-01-2022

How to Cite

Ali J, A., ALJAHDHAMI, T. A. S. ., Al-Habsi, I. N. A. ., & Al-Suti, A.-S. Y. A. . (2022). Brain Tumor Classification using Framelet Transform based energy features and K-Nearest Neighbor classifier. Journal of Student Research. Retrieved from https://www.jsr.org/index.php/path/article/view/1556