Brain tumour Detection Using Deep Models

Authors

  • Prof. S. Narayana Reddy  Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India
  • B. Venkata Raju  Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRST52310248

Keywords:

Brain Tumour, Convolutional Variational Auto Encoder, latent space, Synthetic- images, Generator model, Classifier Model.

Abstract

Brain tumour is a kind of tumour which affects the brain tissue and spreads along time varying regenerative disease. If not detected in particular time limit it will be out of control and results in further death. So, instead of neglecting this disease we can detect this disease using our method computer aided diagnosis where doctor / radiologist is absent. If this brain tumour is detected in early stage is sometimes be cured. In this project we introduced two deep learning models. primary model is for converting small unbalanced dataset to large balanced dataset of MRI images i.e.., using Modified Convolutional Variational Auto Encoder(CVAE). The second Model is used for detection and classification. The first model is generative model and another model is used for Training and classification using the classifier of Residual Network Model (RESNET). By using these two deep models higher Performance of model is achieved. The proposed framework has an accuracy of 99%

References

  1. Wessam M. Salama , Ahmed Shokry “A novel framework for brain tumour detection based on convolutional variational generative models” In: Multimedia Tools and Applications (2022) 81:16441–16454
  2. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2019) Brain tumour classification using convolutional neural network. In: World congress on medical physics and biomedical engineering 2018. Springer, pp 183–189
  3. Ahmed KB, Hall LO, Goldgof DB, Liu R, Gatenby RA (2017) Fine-tuning convolutional deep features for mri based brain tumour classification. In: Medical imaging 2017: computer-aided diagnosis, vol 10134. international society for optics and photonics, p 101342e
  4. Deepak S, Ameer P (2019) Brain tumour classification using deep cnn features via transfer learning. Comput Biol Med 111:103345
  5. Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumour classification. IEEE Access 7:36266–36273
  6. Guo X, Yin Y, Dong C, Yang G, Zhou G (2008) On the class imbalance problem. In: 2008 Fourth international conference on natural computation, vol 4. IEEE, pp 192–201
  7. Isensee F, Maier-hein KH (2021) Nnu-net for brain tumour segmentation. In: Brainlesion: glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II, vol 12658Springer Nature, p 118
  8. Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147–159
  9. Chakrabarty N (2019) Dataset for brain tumour mri images dataset from kaggle web site. (Brain MRI Images for Brain Tumour Detection | Kaggle)
  10. Kumar S, Dabas C, Godara S (2017) Classification of brain mri tumour images: a hybrid approach. Procedia Comput Sci 122:510–517
  11. Liu R, Hall LO, Goldgof DB, Zhou M, Gatenby RA, Ahmed KB (2016) Exploring deep features from brain tumour magnetic resonance images via transfer learning. In: 2016 International joint conference on neural networks (IJCNN). IEEE, pp 235–242
  12. Lo SCB, Chan HP, Lin JS, Li H, Freedman MT, Mun SK (1995) Artificial convolution neural network for medical image pattern recognition. Neural Netw 8(7-8):1201–1214
  13. Mohan G, Subashini MM (2018) Mri based medical image analysis: Survey on brain tumour grade classification. Biomedical Signal Processing and Control 39:139–161
  14. Doersch C (2016) Tutorial on variational autoencoders. arXiv:1606.05908
  15. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumour classification using deep cnn with extensive data augmentation. J Computat Sci 30:174–182
  16. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
  17. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv:1312.6114
  18. Zhang D, Huang G, Zhang Q, Han J, Han J, Wang Y, Yu Y (2020) Exploring task structure for brain tumour segmentation from multi-modality mri images. IEEE Trans Image Process 29:9032–9043
  19. Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2021) Cross-modality deep feature learning for brain tumour segmentation. Pattern Recogn 110:107562.

International Journal of Scientific Research in Science and Technology

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Published

2023-04-30

Issue

Volume 10, Issue 2, March-April-2023

Section

Research Articles

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Copyright (c) IJSRST

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. S. Narayana Reddy, B. Venkata Raju "Brain tumour Detection Using Deep Models" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.321-327, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST52310248