Brain Tumor Detection Using RNN

1. Anandan, P. and Sabeenian, R.S. (2016) Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and
Arithmetic Coding. Circuits and Systems, 7, 2059-2069
2. Li, Y.C., Yang, Q. and Jiao, R.H. (2010) Image Compression Scheme Based on Curvelet Transform and Support Vector MAchine.
Expert Systems with Applications, 4, 3063-3069
3. GeethuMohana, M. Monica Subashini, “MRI based medical image analysis: Survey on brain tumor grade classification”,
Biomedical Signal Processing and Control 39 (2018) 139–161M. Young, The Technical Writer’s Handbook. Mill Valley, CA:
University Science.
4. Eman Abdel-Maksoud et al, “Brain tumor segmentation based on a hybrid clusteringtechnique”, Egyptian Informatics Journal
(2015) 16, 71–81
5. Shin Hoo-Chang et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset
Characteristics and Transfer Learning”, IEEE Trans Med Imaging. 2016 May; 35(5): 1285–1298
6. R. Abdulraqeb et al., “An Automated Method for Segmenting Brain Tumors on MRI Images”, Biomedical Engineering, Vol. 51, No.
2, July, 2017, pp. 97-101.
7. H C Sateesh Kumar et al., “Automatic Image Segmentation using Wavelets”, IJCSNS International Journal of Computer Science and
Network Security, VOL.9 No.2, February2009, PP: 305-313.
8. K.A.HeliniKulasuriya et al., “ Forecasting Epileptic Seizures using EEG signals, wavelet transform and Artificial Neural Networks”
978- 1-61284-704-7/11/$26.00 ©2011IEEE, pp557-562
9. Gurbinder Kaur, Balwinder Singh, “Intensity Based Image Segmentation Using Wavelet Analysis and Clustering Techniques”
Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2 No. 3 Jun-Jul 2011,379-384.
10. Wang, G.; Zuluaga, M.A.; Pratt, R.; Aertsen, M.; Doel, T.; Klusmann, M.; Ourselin, S. Slic-Seg: A minimally interactive segmentation of the placenta from op.