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Automatic CAD System for Brain Diseases Classification Using CNN-LSTM Model

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Emerging Technologies in Data Mining and Information Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1348))

Abstract

Development portrayal is a troublesome investigation issue in the space of therapeutic science. The frontal cortex development is fundamentally fundamental and envisions a threat to life. We propose a novel and totally automated brain development portrayal CAD system using significant learning estimations. The implemented model involves steps like pre-planning, division, features planning, and portrayal. In division, we revolve around discovering the ROI of tainted regions using dynamic thresholding. We dealt with the pre-taken care of MR image to the generous CNN model for modified features extraction using the pre-arranged ResNet50 model. The isolated components are, moreover, lessened using the principal component analysis (PCA). We plan the long short-term memory (LSTM) classifier to vanquish the dissipating point issue. The vanishing point issue of neural association classifiers prompts request botches.

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References

  1. E.J. Rushing, WHO classification of tumors of the nervous system: preview of the upcoming 5th edition. Memo 14, 188–191 (2021). https://doi.org/10.1007/s12254-021-00680-x

  2. D.N. Louis, A. Perry, G. Reifenberger et al., The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131, 803–820 (2016). https://doi.org/10.1007/s00401-016-1545-1

    Article  Google Scholar 

  3. Ö. Yapıcıer, Pathology, epidemiology, and WHO classification of brain tumors, in Atlas of clinical cases on brain tumor imaging, ed. by Y. Özsunar, U. Şenol (Springer, Cham, 2020). https://doi.org/10.1007/978-3-030-23273-3_1

  4. R. Banan, C. Hartmann, The new WHO 2016 classification of brain tumors—what neurosurgeons need to know. Acta Neurochir. 159, 403–418 (2017). https://doi.org/10.1007/s00701-016-3062-3

    Article  Google Scholar 

  5. B. Menze et al., The Multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  6. N. Gordillo, E. Montseny, P. Sobrevilla, State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31(8), 1426–1438 (2013)

    Article  Google Scholar 

  7. D. Bhattacharyya, T. Kim, Brain tumor detection using MRI image analysis, in Ubiquitous computing and multimedia applications. UCMA 2011, ed. by T. Kim, H. Adeli, R.J. Robles, M. Balitanas. Communications in Computer and Information Science, vol. 151 (Springer, Berlin, Heidelberg, 2011). https://doi.org/10.1007/978-3-642-20998-7_38

  8. M.A.A. Hamid, N.A. Khan, Investigation and classification of MRI brain tumors using feature extraction technique. J. Med. Biol. Eng. 40, 307–317 (2020). https://doi.org/10.1007/s40846-020-00510-1

    Article  Google Scholar 

  9. N. Varuna Shree, T.N.R. Kumar, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf. 5, 23–30 (2018). https://doi.org/10.1007/s40708-017-0075-5

  10. H. Jalab, A. Hasan, Magnetic resonance imaging segmentation techniques of brain tumors: a review. J. Neurosci. (2019)

    Google Scholar 

  11. K. Venu, P. Natesan, N. Sasipriyaa, S. Poorani, Review on brain tumor segmentation methods using convolution neural network for MRI images, in: 2018 International conference on intelligent computing and communication for smart world (I2C2SW) (2018). https://doi.org/10.1109/i2c2sw45816.2018.8997387

  12. R. Parvataneni, M.Y. Polley, T. Freeman et al., Identifying the needs of brain tumor patients and their caregivers. J. Neurooncol. 104, 737–744 (2011). https://doi.org/10.1007/s11060-011-0534-4

    Article  Google Scholar 

  13. M. Rahman, I. Parney, Journal of neuro oncology: immunotherapy for brain tumors. J. Neurooncol. 151, 1 (2021). https://doi.org/10.1007/s11060-020-03589-3

    Article  Google Scholar 

  14. M. Soltaninejad, G. Yang, T. Lambrou et al., Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. CARS 12, 183–203 (2017). https://doi.org/10.1007/s11548-016-1483-3

    Article  Google Scholar 

  15. A. Edalati-rad, M. Mosleh, Improving brain tumor diagnosis using MRI segmentation based on collaboration of beta mixture model and learning automata. Arab J. Sci. Eng. 44, 2945–2957 (2019). https://doi.org/10.1007/s13369-018-3320-1

    Article  Google Scholar 

  16. Z. Akkus, A. Galimzianova, A. Hoogi et al., Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30, 449–459 (2017). https://doi.org/10.1007/s10278-017-9983-4

    Article  Google Scholar 

  17. R.A. Zeineldin, M.E. Karar, J. Coburger et al., DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int. J. CARS 15, 909–920 (2020). https://doi.org/10.1007/s11548-020-02186-z

    Article  Google Scholar 

  18. M.I. Sharif, M.A. Khan, M. Alhussein et al., A decision support system for multimodal brain tumor classification using deep learning. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00321-0

    Article  Google Scholar 

  19. S. Kokkalla, J. Kakarla, I.B. Venkateswarlu et al., Three-class brain tumor classification using deep dense inception residual network. Soft. Comput. 25, 8721–8729 (2021). https://doi.org/10.1007/s00500-021-05748-8

    Article  Google Scholar 

  20. R. Ranjbarzadeh, A. Bagherian Kasgari, S. Jafarzadeh Ghoushchi et al., Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci. Rep. 11, 10930 (2021). https://doi.org/10.1038/s41598-021-90428-8

    Article  Google Scholar 

  21. N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, M. Imran, M. Shoaib, A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access, 1–1 (2020) https://doi.org/10.1109/access.2020.2978629

  22. J. Kang, Z. Ullah, J. Gwak, MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors 21(6), 2222 (2021). https://doi.org/10.3390/s21062222

    Article  Google Scholar 

  23. H.M. Rai, K. Chatterjee, Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images. Mach. Learn. Appl. 2, 100004 (2020). https://doi.org/10.1016/j.mlwa.2020.100004

    Article  Google Scholar 

  24. A. Zadeh Shirazi, E. Fornaciari, M.D. McDonnell, M. Yaghoobi, Y. Cevallos, L. Tello-Oquendo, D. Inca, G.A. Gomez, The application of deep convolutional neural networks to brain cancer images: a survey. J. Pers. Med. 10(4), 224 (2020). https://doi.org/10.3390/jpm10040224

    Article  Google Scholar 

  25. S.A.Y. Al-Galal, I.F.T. Alshaikhli, M. Abdulrazzaq, MRI brain tumor medical images analysis using deep learning techniques: a systematic review. Health Technol. 11, 267–282 (2021). https://doi.org/10.1007/s12553-020-00514-6

    Article  Google Scholar 

  26. W. Wu, D. Li, J. Du, X. Gao, W. Gu, F. Zhao, … H. Yan, An intelligent diagnosis method of brain MRI tumor segmentation using deep convolutional neural network and SVM algorithm. Comput. Math. Methods Med. 2020, 1–10 (2020).https://doi.org/10.1155/2020/6789306

  27. P.K. Mallick, S.H. Ryu, S.K. Satapathy, S. Mishra, N.G. Nguyen, P. Tiwari, Brain MRI image classification for cancer detection using deep wavelet autoencoder based deep neural network. IEEE Access, 1–1 (2019).https://doi.org/10.1109/access.2019.2902252

  28. M. Sajjad, S. Khan, K. Muhammad, W. Wu, A. Ullah, S.W. Baik, Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. (2018). https://doi.org/10.1016/j.jocs.2018.12.003

    Article  Google Scholar 

  29. E. Irmak, Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran. J. Sci. Technol. Trans. Electr. Eng. 45(3), 1015–1036 (2021). https://doi.org/10.1007/s40998-021-00426-9

    Article  Google Scholar 

  30. J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, … Q. Feng, Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLOS ONE, 10(10), e0140381 (2015). https://doi.org/10.1371/journal.pone.0140381

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Authors and Affiliations

  1. Kalinga University, Raipur, India

    Deipali Vikram Gore & Ashish Kumar Sinha

  2. Vishwakarma Institute of Information Technology, Pune, India

    Vivek Deshpande

Authors
  1. Deipali Vikram Gore
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  2. Ashish Kumar Sinha
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  3. Vivek Deshpande
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Corresponding author

Correspondence to Deipali Vikram Gore .

Editor information

Editors and Affiliations

  1. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, India

    Paramartha Dutta

  2. Department of Computer Application and Science, Institute of Engineering and Management, Kolkata, West Bengal, India

    Abhishek Bhattacharya

  3. Institute of Engineering and Management, Kolkata, India

    Soumi Dutta

  4. Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Wen-Cheng Lai

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Gore, D.V., Sinha, A.K., Deshpande, V. (2023). Automatic CAD System for Brain Diseases Classification Using CNN-LSTM Model. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_54

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