Skip to main content
SpringerLink
Log in

A Deep Learning Techniques for Brain Tumor Severity Level (K-CNN-BTSL) Using MRI Images

  • Chapter
  • First Online:
Artificial Intelligence for Societal Issues

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 231))

  • 252 Accesses

Abstract

Brain tumor is revealed due to uncontrolled rapid growth of cells. If not found at an infinity stage, it causes death. Early stage of prediction of diseases of Human’s Brain overcomes a lot of issues. Still classification and segmentation of Brain tumor is very challenging even though having existing promising solutions. Magnetic resonance imaging (MRI) is one forefront technique which is suitable for identifying disease for those who are sufferers. This research focuses on Deep learning techniques for identifying the risk of Brain Tumor and also explores the Brain Tumor detection, segmentation, classification and prediction of severity level of Brain tumor. In this work, MRI The training and testing is done by the CNN from MRI images. The Convolution Neural Network Model is used to classify the images and K-means algorithm is applied to segment the image. Feature extraction applied through Discrete Wavelet Transform and Principal Component Analysis to reduce the dimensional for better accuracy and computational power management. The experiment with deep learning algorithms indicates the effectiveness of the proposed work. A novel method of hybridization of K-Neighboring Algorithm and CNN (K-CNN-BTSL) predicts the severity level of a brain tumor either benign or malignant at an early point with 93% accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Challa, S.K., Kumar, A. Semwal, V.B.: A for human activity recognition using wearable sensor data. Vis. Comput. (2021)

    Google Scholar 

  2. Jain, R., Semwal, V.B., Kaushik, P.: Deep ensemble learning approach for lower extremity activities recognition using wearable sensors. Expert Syst. e12743 (2021)

    Google Scholar 

  3. Semwal, V.B., Lalwani, P., Mishra, M.K. et al.: An optimized feature selection using bio-geography optimization technique for human walking activities recognition. Computing (2021)

    Google Scholar 

  4. Benson, C.C., Lajish, V.L., Rajamani, K.: Brain tumor extraction from MRI brain images using marker based watershed algorithm. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 318–323. IEEE (2015)

    Google Scholar 

  5. Benson, C.C., Lajish, V.L.: Morphology based enhancement and skull stripping of MRI brain images. In: 2014 International Conference on Intelligent Computing Applications, pp. 254–257. IEEE (2014)

    Google Scholar 

  6. Huang, M., Yang, W., Yao, W., Jiang, J., Chen, W., Feng, Q.: Brain tumor segmentation based on local independent projection- based classification. IEEE Trans. Biomed. Eng. 61(10), 2633–2645 (2014)

    Google Scholar 

  7. Semwal, V.B. et al.: Speed, cloth and pose invariant gait recognition-based person identification. In: Machine Learning: Theoretical Foundations and Practical Applications (2019)

    Google Scholar 

  8. Sapra, P., Singh, R., Khurana, S.: Brain tumor detection using neural network. Int. J. Sci. Mod. Eng. (IJISME) (2013). ISSN: 2319-6386

    Google Scholar 

  9. Jemimma, T.A., Jacob Vetharaj, Y.: Watershed algorithm based DAPP features for brain tumor segmentation and classification. In: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 155–158. IEEE (2018)

    Google Scholar 

  10. George, D.N., Jehlol, H.B., Oleiwi, A.S.A.: Brain tumor detection using shape features and machine learning algorithms. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5(10), 454–459 (2015)

    Google Scholar 

  11. Cui, B., Xie, M., Wang C.: A deep convolutional neural network learning transfer to SVM-based segmentation method for brain tumor. In: 2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT), pp. 1–5. IEEE (2019)

    Google Scholar 

  12. Rashid, M.H.O., Mamun, M.A., Hossain, M.A., Uddin, M.P.: Brain tumor detection using anisotropic filtering, SVM classifier and morphological operation from MR images. In: 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pp. 1–4. IEEE (2018)

    Google Scholar 

  13. Abdulbaqi, H.S., Mat, M.Z., Omar, A.F., Mustafa, I.S.B., Abood, L.K.: Detecting brain tumor inmagnetic resonance images using hidden Markov random fields and threshold techniques. In: 2014 IEEE Student Conference on Research and Development, pp. 1–5 (2014)

    Google Scholar 

  14. Ali, A.H., Al-hadi, S.A., Naeemah, M.R., Mazher, A.N.: Classification of brain lesion using K-nearest neighbor technique and texture analysis. J. Phys. Conf. Ser. 01 (2018); Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Google Scholar 

  15. Nabizadeh, N., Kubat, M.: Brain tumors detection and segmentation in MR images: gabor wavelet versus statistical features. Comput. Electr. Eng. 45, 286–301 (2015)

    Google Scholar 

  16. Supot, S., Thanapong, C., Chuchart, P., Manas, S.: Segmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering. In: 2007 IEEE International Conference on Integration Technology, pp. 697–700 (2007)

    Google Scholar 

  17. Fletcher-Heath, L.M., Hall, L.O., Goldgof, D.B., Murtagh, F.R.: Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif. Intell. Med. 21, 43–63 (2001)

    Google Scholar 

  18. Singh, A.: Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM. In: 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 98–102. IEEE (20150

    Google Scholar 

  19. YasminM, Mohsin S., Sharif, M., Raza, M., Masood, S.: Brain image analysis: a survey. World Appl. Sci. J. 19, 1484–1494 (2012)

    Google Scholar 

  20. Irum, I., Sharif, M., Raza, M., Yasmin, M.: Salt and pepper noise removal filter for 8-bit images based on local and global occurrences of grey levels as selection indicator. Nepal J. Sci. Technol. 15, 123–132 (2014)

    Google Scholar 

  21. Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M., Silva, C.A.: Brain tumour segmentation based on extremely randomized forest with high-level features. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3037–3040. IEEE (2015)

    Google Scholar 

  22. Semwal, V.B., Mondal, K., Nandi, G.C.: Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput. Appl. 28(3), 565–574 (2017)

    Google Scholar 

  23. Irum, I., Sharif, M., Yasmin, M., Raza, M., Azam, F.: A noise adaptive approach to impulse noise detection and reduction. Nepal J. Sci. Technol. 15, 67–76 (2014)

    Google Scholar 

  24. Lu, X., Huang, Z., Yuan, Y.: MR image super-resolution via manifold regularized sparse learning. Neurocomputing 162, 96–104 (2015)

    Google Scholar 

  25. Sawakare, S., Chaudhari, D.: Classification of brain tumor using discrete wavelet transform, principal component analysis and probabilistic neural network. Int. J. Res. Emerg. Sci. Technol. 1(6), 2349–2761 (2014)

    Google Scholar 

  26. Mathew, A.R., Babu Anto, P.: Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: 2017 International Conference on Signal Processing and Communication (ICSPC), pp. 75–78. IEEE (2017)

    Google Scholar 

  27. Sarhan, A.M.: Brain tumor classification in magnetic resonance images using deep learning and wavelet transform. J. Biomed. Sci. Eng. 13(06), 102 (2020)

    Google Scholar 

  28. Arizmendi, C., Vellido, A., Romero, E.: Binary classification of brain tumours using a discrete wavelet transform and energy criteria. In: 2011 IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4. IEEE (2011)

    Google Scholar 

  29. Pratiwi, Mellisa, Harefa, Jeklin, Nanda, Sakka: Mammograms classification using gray-level co-occurrence matrix and radial basis function neural network. Procedia Comput. Sci. 59, 83–91 (2015)

    Google Scholar 

  30. Badža, M.M., Barjaktarović, M.: Classification of brain tumors from MRI images using a convolutional neural network. Appl. Sci. 10(6), 1999 (2020)

    Google Scholar 

  31. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556

  32. Sawakare, S., Chaudhari, D.: Classification of brain tumor using discrete wavelet transform, principal component analysis and probabilistic neural network. Int. J. Res. Emerg. Sci. Technol. 1(6), 2349–2761 (2014)

    Google Scholar 

  33. Mathew, A.R., Babu Anto, P.: Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: 2017 International Conference on Signal Processing and Communication (ICSPC), pp. 75–78. IEEE (2017)

    Google Scholar 

  34. Sarhan, A.M.: Brain tumor classification in magnetic resonance images using deep learning and wavelet transform. J. Biomed. Sci. Eng. 13(06), 102 (2020)

    Google Scholar 

  35. Arizmendi, C., Vellido, A., Romero, E.: Binary classification of brain tumours using a discrete wavelet transform and energy criteria. In: 2011 IEEE Second Latin American Symposium on Circuits and Systems (LASCAS), pp. 1–4. IEEE (2011)

    Google Scholar 

  36. Semwal, V.B. et al.: Pattern identification of different human joints for different human walking stylesusing inertial measurement unit (IMU) sensor. Artif. Intell. Rev. 1–21 (2021)

    Google Scholar 

  37. Dua, N., Singh, S.N., Semwal, V.B.: Multi-input CNN-GRU based human activity recognition using wearable sensors. Computing (2021)

    Google Scholar 

  38. Bijalwan, V., Semwal, V.B., Gupta, V.: Wearable sensor-based pattern mining for human activity recognition: deep learning approach. Ind. Robot.: Int. J. Robot. Res. Appl. (2021)

    Google Scholar 

  39. Abbasi, S., Tajeripour, F.: Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526–535 (2017)

    Google Scholar 

  40. Sauwen, N., Acou, M., Sima, D.M., Veraart, J., Maes, F., Himmelreich, U., et al.: Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. BMC Med. Imaging 17, 29 (2017)

    Google Scholar 

  41. Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Garcia-Perez, A., de Jesus Romero-Troncoso, R., Aguirre-Ramos, H., Cruz-Aceves, I., Chalopin, C.: Localized active contour model with background intensity compensation applied on automaticMRbrain tumor segmentation. Neurocomputing 220, 84–97

    Google Scholar 

  42. Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Garcia-Perez, A., de Jesus Romero-Troncoso, R., Aguirre-Ramos, H., Cruz-Aceves, I., Chalopin, C.: Localized active contour model with background intensity compensation applied on automaticMRbrain tumor segmentation. Neurocomputing 220, 84–97

    Google Scholar 

  43. Akbar, S., Akram, M.U., Sharif, M.,Tariq, A., Khan, S.A.: Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif. Intell. Med. 90, 15–24 (2018)

    Google Scholar 

  44. Banerjee, S., Mitra, S., Shankar, B.U.: Automated 3D segmentation of brain tumor using visual saliency. Inf. Sci. 424, 337–353 (2018)

    Google Scholar 

  45. Raja, N.S.M., Fernandes, S.L., Dey, N., Satapathy, S.C., Rajinikanth, V.: Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. J. Ambient. Intell. Human Comput. 1–12 (2018)

    Google Scholar 

  46. Subudhi, A., Dash, M., Sabut, S.: Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybern. Biomed. Eng. 40, 277–289 (2020)

    Google Scholar 

  47. Gupta, N., Bhatele, P., Khanna, P.: Glioma detection on brain MRIs using texture and morphological features with ensemble learning. Biomed. Signal Process Control 47, 115–125 (2019)

    Google Scholar 

  48. Myronenko, A., Hatamizadeh, A.: Robust semantic segmentation of brain tumor regions from 3D MRIs (2020). arXiv:2001.02040

Download references

Author information

Authors and Affiliations

  1. Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

    M. Saravanan, Saksham Bhardwaj, Chakshusman Mishr & Rohit Parthasarathy

  2. School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, India

    Suseela Sellamuthu

Authors
  1. M. Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Suseela Sellamuthu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saksham Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chakshusman Mishr
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rohit Parthasarathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to M. Saravanan or Suseela Sellamuthu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute Of Technology Silchar, Cachar, Assam, India

    Anupam Biswas

  2. National Institute of Technology Bhopal, Bhopal, Madhya Pradesh, India

    Vijay Bhaskar Semwal

  3. Department of Computer Science, PDPM, Indian Institute of Information Technology, Jabalpur, India

    Durgesh Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saravanan, M., Sellamuthu, S., Bhardwaj, S., Mishr, C., Parthasarathy, R. (2023). A Deep Learning Techniques for Brain Tumor Severity Level (K-CNN-BTSL) Using MRI Images. In: Biswas, A., Semwal, V.B., Singh, D. (eds) Artificial Intelligence for Societal Issues. Intelligent Systems Reference Library, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-031-12419-8_14

Download citation

Publish with us

Policies and ethics

Search

Navigation