Skip to main content
SpringerLink
Log in

MRI Image Segmentation: Brain Tumor Detection and Classification Using Machine Learning

  • Conference paper
  • First Online:
Proceedings of Data Analytics and Management (ICDAM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 786))

Included in the following conference series:

Abstract

Health care is being transformed by machine learning (ML) solutions and artificial intelligence (AI). Healthcare providers must improve patients’ conditions, cut cost expenses, increase efficiency, and make use of the most recent advances in pharmacological therapy if they are to be more effective. In order to modernize the healthcare system, it is necessary to unlock the billions of countless datasets that are typically hidden away in faulty digital systems. The jobs of staff members and physicians can be automated or enhanced with the use of artificial intelligence (AI), which can bring value. Artificial intelligence will entirely automate many monotonous processes, enhancing the efficiency of health practitioners, and the quality of patient outcomes. The use of AI in health care can speed up and reduce the cost of patient care by process automation and analysis of massive patient datasets. AI will have a huge impact on bringing forth more efficiency and innovations in healthcare ecosystem modernization than we can now imagine. This study’s primary goal is to examine the past research on tumor detection along with the future scope that can be taken up by the researchers. Therefore, this research is based on convolution neural networks (CNN), having the capability to examine large amounts of complex images and perform classifications. The proposed method for classifying brain images using CNN delivers an excellent classification efficiency when compared to existing classifiers. CNN has been used as it acts as a strong tool for disease detection and helps doctors in perfect treatment plans with an accuracy of 96.15% and therefore increases the chances of disease prevention.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Kumar M, Singhal S, Shekhar S, Sharma B, Srivastava G (2022) Optimized stacking ensemble learning model for breast cancer detection and classification using machine learning. Sustainability 14(21):13998

    Article  Google Scholar 

  2. Sharma A, Yadav DP, Garg H, Kumar M, Sharma B, Koundal D (2021) Bone cancer detection using feature extraction-based machine learning models. Comput Math Methods Med 2021

    Google Scholar 

  3. AI in healthcare is making our world healthier. Available on: https://www.forbes.com/sites/cindygordon/2022/10/31/ai-in-healthcare-is-making-our-world-healthier/?sh=48ce07013ea9

  4. Dwivedi M, Tiwari RG, Ujjwal N (2022) Deep learning methods for early detection of monkeypox skin lesion. 2022 8th International conference on signal processing and communication (ICSC). Noida, India, pp 343–348. https://doi.org/10.1109/ICSC56524.2022.10009571

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

  6. Widhiarso W, Yohannes Y, Prakarsah C (2018) Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. IJEIS (Indonesian J Electron Instrum Syst) 8:179–190. https://doi.org/10.22146/ijeis.34713

    Article  Google Scholar 

  7. Simaiya S, Lilhore UK, Prasad D, Verma DK (2021) MRI brain tumor detection & image segmentation by hybrid hierarchical k-means clustering with FCM based machine learning model. Ann R.S.C.B. 25(1):88–94. ISSN:1583-6258

    Google Scholar 

  8. Brindha PG, Kavinraj M, Manivasakam P, Prasanth P (2021) Brain tumor detection from MRI images using deep learning techniques. IOP Conf Ser: Mater Sci Eng 1055:012115. IOP Publishing. https://doi.org/10.1088/1757-899X/1055/1/012115

  9. Sharma P, Diwakar M, Choudhary S (2012) Application of edge detection for brain tumor detection. Int J Comput Appl 58(16):0975–8887

    Google Scholar 

  10. Sapra P, Singh R, Khurana S (Aug 2013) Brain tumor detection using neural network. Int J Sci Mod Eng (IJISME) 1(9). ISSN:2319-6386

    Google Scholar 

  11. Sharma K, Kaur A, Gujral S (2014) Brain tumor detection based on machine learning algorithms. Int J Comput Appl 103(1):0975–8887

    Google Scholar 

  12. Hemanth DJ, Selvathi D, Jude A (2009) Effective fuzzy clustering algorithm for abnormal MR brain image segmentation. In: Advance computing conference 2009. IACC 2009. IEEE international, pp 609–614

    Google Scholar 

  13. Maiti I, Chakraborty M (2012) A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV color model. In: 2012 national conference on computing and communication systems (NCCCS)

    Google Scholar 

  14. Abdullah A et al (2012) Implementation of an improved cellular neural network algorithm for brain tumor detection. In: 2012 international conference on biomedical engineering (Isobel), pp 611–615

    Google Scholar 

  15. Chrutha S, Jayashree MJ (2014) An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. In: 2014 international conference on control instrumentation, communication and computational technology (ICCICCT), pp. 1193–1199

    Google Scholar 

  16. Karuna M, Joshi A (Oct 2013) Automatic detection and severity analysis of brain tumors using GUI in matlab. IJRET: Int J Res Eng Technol 02(10). ISSN: 2319-1163

    Google Scholar 

  17. Dubey RB, Hanmandlu M, Vasikarla S (2011) Evaluation of three methods for MRI brain tumor segmentation. IEEE computer society, ITNG 92

    Google Scholar 

  18. Senthilkumaran N, Thimmiaraja J (2014) Histogram equalization for image enhancement using MRI brain images. IEEE CPS, medicine, computer science 2014 world congress on computing and communication technologies (WCCCT), p 45

    Google Scholar 

  19. Borole VY, Nimbhore SLS, Dr Kawthekar SS (Sept–Oct 2015) Image processing techniques for brain tumor detection: a review. Int J Emerg Trends Technol Comput Sci (IJETTCS) 4(5):2. ISSN 2278–6856

    Google Scholar 

  20. R. Nalbalwar, U. Majhi, R. Patil, Prof.S. Gonge. “Detection of Brain Tumor by using ANN”, International Journal of Research in Advent Technology, Vol.2, No.4, April 2014 E-ISSN: 2321–9637.

    Google Scholar 

  21. Jafari M, Shafaghi R (2012) A hybrid approach for automatic tumor detection of brain MRI using support vector machine and genetic algorithm. Global J Sci, Eng Technol 3:1–8. https://doi.org/10.5120/18036-6883

    Article  Google Scholar 

  22. Sumitra N, Saxena RK (2013) Brain tumor classification using back propagation neural network. Int J Image Graph Signal Process 5:45. https://doi.org/10.5815/ijigsp.2013.02.07

    Article  Google Scholar 

  23. Xing F, Xie Y, Yang L. An automatic learning-based framework for robust nucleus segmentation. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2015.2481436

  24. Bavirisetti DP, Kumar KV, Gang X, Dhuli R (2017) Fusion of MRI and CT images using guided image filter and image statistics. Int J Imaging Syst Technol 27(3):227–237

    Article  Google Scholar 

  25. Public dataset from Kaggle is used. https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection

  26. Alqudah AM, Alquraan H, Qasmieh IA, Alqudah A, Al-Sharu W (Nov–Dec 2019) Brain tumor classification using deep learning technique—a comparison between cropped, uncropped, and segmented lesion images with different sizes. Int J Adv Trends Comput Sci Eng 8(6):3684–3691

    Google Scholar 

  27. Layers on CNN. https://www.analyticsvidhya.com/blog/2021/06/image-processing-using-cnn-a-beginners-guide/

  28. Yadav DP, Sharma A, Athithan S, Bhola A, Sharma B, Dhaou IB (2022) Hybrid SFNet model for bone fracture detection and classification using ML/DL. Sensors 22(15):5823

    Article  Google Scholar 

  29. Performance evolution of CNN. https://data-flair.training/blogs/brain-tumor-classification-machine-learning/

Download references

Author information

Authors and Affiliations

  1. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Sristhi Mahajan, Pradeepta Kumar Sarangi, Lekha Rani & Dilbaag Singh

  2. Graphic Era Hill University, Dehradun, India

    Ashok Kumar Sahoo

Authors
  1. Sristhi Mahajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashok Kumar Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pradeepta Kumar Sarangi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lekha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dilbaag Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pradeepta Kumar Sarangi .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India

    Abhishek Swaroop

  2. Jan Wyżykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Polytechnic Institute of Portalegre, Portalegre, Portugal

    Sérgio Duarte Correia

  4. Centre for Communications Technology, London Metropolitan University, London, UK

    Bal Virdee

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mahajan, S., Sahoo, A.K., Sarangi, P.K., Rani, L., Singh, D. (2024). MRI Image Segmentation: Brain Tumor Detection and Classification Using Machine Learning. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 786. Springer, Singapore. https://doi.org/10.1007/978-981-99-6547-2_11

Download citation

Publish with us

Policies and ethics

Search

Navigation