Skip to main content
Springer Nature Link
Log in

A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images

  • Published:
Save article
View saved research
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Brain tumors pose a significant health concern globally, with their detection and diagnosis being crucial for timely intervention and treatment planning. These abnormal growths can develop within the brain or originate from other parts of the body, spreading to the brain. They can be benign or malignant, and their impact on cognitive and physical function can vary widely depending on their location, size, and type. Detecting brain tumors involves a combination of imaging techniques and clinical assessment. Magnetic Resonance Imaging (MRI) is one of the primary imaging modalities used for this purpose due to its ability to provide detailed images of the brain's anatomy and pathology. Advanced image processing techniques and machine learning algorithms play an increasingly important role in enhancing the accuracy and efficiency of brain tumor detection. This study presents a comprehensive approach for enhancing brain tumor detection in MRI images using a combination of advanced techniques. Firstly, an improved K-means clustering algorithm is introduced to segment tumor regions effectively. Following this, Median Filtering is applied to refine image quality and reduce noise interference. Subsequently, a deep neural network architecture is employed for precise tumor classification utilizing the BRATS dataset. The proposed methodology aims to improve the accuracy and efficiency of brain tumor detection, offering potential advancements in medical image analysis and diagnosis. Experimental results demonstrate promising outcomes, indicating the effectiveness of the proposed approach in accurately identifying tumor regions in MRI scans.

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

Access this article

Log in via an institution

Subscribe and save

Springer+
from €37.37 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price includes VAT (Netherlands)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
The alternative text for this image may have been generated using AI.
Fig. 2
The alternative text for this image may have been generated using AI.
Fig. 3
The alternative text for this image may have been generated using AI.
Fig. 4
The alternative text for this image may have been generated using AI.
Fig. 5
The alternative text for this image may have been generated using AI.
Fig. 6
The alternative text for this image may have been generated using AI.
Fig. 7
The alternative text for this image may have been generated using AI.
Fig. 8
The alternative text for this image may have been generated using AI.
Fig. 9
The alternative text for this image may have been generated using AI.
Fig. 10
The alternative text for this image may have been generated using AI.
Fig. 11
The alternative text for this image may have been generated using AI.
Fig. 12
The alternative text for this image may have been generated using AI.
Fig. 13
The alternative text for this image may have been generated using AI.
Fig. 14
The alternative text for this image may have been generated using AI.
Fig. 15
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

Data availability

The brain MRI image data used to support the findings of this study is publicly available in the repositories (https://www.kaggle.com/datasets/awsaf49/brats2020-training-data).

References

  1. Zhang W, Yong Wu, Yang Bo, Shunbo Hu, Liang Wu, Dhelim S (2021) Overview of multi-modal brain tumor mr image segmentation. Healthcare 9(8):1051

    Article  Google Scholar 

  2. Prakash Tunga P, Singh V (2021) Compression of MRI brain images based on automatic extraction of tumor region. Int J Electrical Comput Engineering 11(5):3964–3976

    Article  Google Scholar 

  3. Gutmann DH, Kettenmann H (2019) Microglia/brain macrophages as central drivers of brain tumor pathobiology. Neuron 104(3):442–449

    Article  Google Scholar 

  4. Srikanth B, Venkata Suryanarayana S. Multi-class classification of brain tumor images using data augmentation with deep neural network. Materials Today: Proceedings. 2021. https://doi.org/10.1016/j.matpr.2021.01.601

  5. Hussain, Ashfaq, and Ajay Khunteta. "Semantic segmentation of brain tumor from MRI images and SVM classification using GLCM features." In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 38–43. IEEE, 2020.

  6. Sankaran KS, Poyyamozhi AS, Ali SS, Jennifer Y. A Conceptual and Effective Scheme for Brain Tumor Identification Using Robust Random Forest Classifier. In Proceedings of International Conference on Information Technology and Applications. Springer. Singapore. 2022. pp. 109-118.

  7. Chaudhary A, Bhattacharjee V (2020) An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT. Int J Inf Technol 12(1):141–148

    Google Scholar 

  8. Zotin A, Simonov K, Kurako M, Hamad Y, Kirillova S (2018) Edge detection in MRI brain tumor images based on fuzzy C-means clustering. Proc Comput Sci 126:1261–1270

    Article  Google Scholar 

  9. Maruthi KD, Satyanarayana D, Prasad MN (2021) MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier. J Ambient Intell Human Comput 12(2):2867–2880

    Article  Google Scholar 

  10. Deepak S, Ameer PM (2021) Automated categorization of brain tumor from mri using cnn features and svm. J Ambient Intell Humaniz Comput 12(8):8357–8369

    Article  Google Scholar 

  11. Chaki J (2022) Two-fold brain tumor segmentation using fuzzy image enhancement and DeepBrainet2. 0. Multimedia Tools Appl 81:30705–30731

    Article  Google Scholar 

  12. Devi RS, Perumal B, Rajasekaran MP (2022) A hybrid deep learning based brain tumor classification and segmentation by stationary wavelet packet transform and adaptive kernel fuzzy c means clustering. Advances in Engineering Software 170:103146

    Article  Google Scholar 

  13. Fang L, Wang X (2023) Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation. Biomed Signal Process Control 79:104027

    Article  Google Scholar 

  14. Bodavarapu PNR, Srinivas PVVS, Mishra P, Mandhala VN, Kim H-J. Optimized deep neural model for cancer detection and classification over ResNet. In Smart Technologies in Data Science and Communication. Springer. Singapore. 2021. pp. 267–280.

  15. Gu X, Shen Z, Xue J, Fan Y, Ni T (2021) Brain tumor MR image classification using convolutional dictionary learning with local constraint. Front Neurosci 15:679847

    Article  Google Scholar 

  16. Adu K, Yongbin Yu, Cai J, Asare I, Quahin J (2022) The influence of the activation function in a capsule network for brain tumor type classification. Int J Imaging Syst Technol 32(1):123–143

    Article  Google Scholar 

  17. Akinyelu AA, Zaccagna F, Grist JT, Castelli M, Rundo L (2022) Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey. J Imaging 8(8):205

    Article  Google Scholar 

  18. Sedik A, El-Hag NA, El-Hoseny HM, El Banby G, Khalaf AA, Abd El-Samie FE, El-Shafai W. Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models. Available at SSRN 4111795.

  19. Toufiq DM, Sagheer AM, Veisi H (2021) Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis. Bullet Electr Eng Inform 10(5):2588–2597

    Article  Google Scholar 

  20. Ahmadi M, Sharifi A, Jafarian Fard M, Soleimani N. Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. Int J Neurosci. 2021:1–12.

  21. Rao CS, Karunakara K (2022) Efficient detection and classification of brain tumor using kernel based SVM for MRI. Multimedia Tools Appl 81(5):7393–7417

    Article  Google Scholar 

  22. Toufiq DM, Sagheer AM, Veisi H (2021) A Review on Brain Tumor Classification in MRI Images. Turkish J Comput Math Educ 12(14):1958–1969

    Google Scholar 

  23. Aminian M, Khotanlou H (2022) CapsNet-based brain tumor segmentation in multimodal MRI images using inhomogeneous voxels in Del vector domain. Multimedia Tools Appl 81(13):17793–17815

    Article  Google Scholar 

  24. Ahmad B, Sun J, You Qi, Palade V, Mao Z (2022) Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks. Biomedicines 10(2):223

    Article  Google Scholar 

  25. Mishra L, Verma S (2022) Graph attention autoencoder inspired CNN based brain tumor classification using MRI. Neurocomputing 503:236–247

    Article  Google Scholar 

  26. Díaz-Pernas FJ, Martínez-Zarzuela M, Antón-Rodríguez M, González-Ortega D (2021) A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare 9(2):153

    Article  Google Scholar 

  27. Kumar A, Manikandan R, Kose U, Gupta D, Satapathy SC. Doctor's Dilemma: Evaluating an Explainable Subtractive Spatial Lightweight Convolutional Neural Network for Brain Tumor Diagnosis. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 2021;17(3s): 1–26.

  28. Nayak DR, Padhy N, Mallick PK, Singh A (2022) A deep autoencoder approach for detection of brain tumor images. Comput Electr Eng 102:108238

    Article  Google Scholar 

  29. Abd El Kader I, Xu G, Shuai Z, Saminu S, Javaid I, Salim Ahmad I (2021) Differential deep convolutional neural network model for brain tumor classification. Brain Sci 11(3):352

    Article  Google Scholar 

  30. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: Deep convolutional neural networks. Fut Gen Comput Syst 87:290–297

    Article  Google Scholar 

  31. Vankdothu R, Hameed MA (2022) Brain tumor MRI images identification and classification based on the recurrent convolutional neural network. Measurement Sensors 24:100412

    Article  Google Scholar 

  32. Shah HA, Saeed F, Yun S, Park JH, Paul A, Kang JM (2022) A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet. IEEE Access 10:65426–65438

    Article  Google Scholar 

  33. Nassar SE, Yasser I, Amer HM, Mohamed MA (2024) A robust MRI-based brain tumor classification via a hybrid deep learning technique. J Supercomput 80(2):2403–2427

    Article  Google Scholar 

  34. Priyadarshini P, Kanungo P, Kar T. Multigrade Brain tumor classification in MRI images using Fine tuned EfficientNet. e-Prime-Advances in Electrical Engineering, Electronics and Energy. 2024:100498.

  35. Khaliki MZ, Başarslan MS (2024) Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN. Sci Rep 14(1):2664

    Article  Google Scholar 

  36. Kumar S, Choudhary S, Jain A, Singh K, Ahmadian A, Bajuri MY (2023) Brain tumor classification using deep neural network and transfer learning. Brain Topography 36(3):305–318

    Article  Google Scholar 

  37. Asif S, Zhao M, Tang F, Zhu Y (2023) An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning. Multimedia Tools Appl 82(20):31709–31736

    Article  Google Scholar 

  38. Rajput IS, Gupta A, Jain V, Tyagi S. A transfer learning-based brain tumor classification using magnetic resonance images. Multimedia Tools Appl. 2023:1–20.

  39. Bairagi VK, Gumaste PP, Rajput SH, Chethan KS (2023) Automatic brain tumor detection using CNN transfer learning approach. Med Biol Eng Comput 61(7):1821–1836

    Article  Google Scholar 

  40. Brain Tumor Segmentation, Dataset from BraTS2020 Competition, available at https://www.kaggle.com/datasets/awsaf49/brats2020-training-data.

  41. Zhang C, Shen X, Cheng H, Qian Q. Brain tumor segmentation based on hybrid clustering and morphological operations. Int J Biomed Imaging. 2019.

  42. Elmontaser NM, Abushaala AM (2022) Brain Tumor Detection in Real MRI Images Based on Otsu and K-means Cluster Algorithms. Brain 21:22

    Google Scholar 

  43. Kshirsagar PR, Yadav AD, Joshi KA, Chippalkatti P, Nerkar RY (2020) Classification and Detection of Brain Tumor by using GLCM Texture Feature and ANFIS. J Res Image Signal Proc 5(1):15–31

    Google Scholar 

  44. Rastogi R, Rastogi AR, Chaturvedi DK, Sagar S, Tandon N. Massive data classification of brain tumors using DNN: opportunity in medical healthcare 4.0 through sensors. In Handbook of Machine Learning for Computational Optimization, pp. 95–111. CRC Press, 2021.

  45. Ahmed AA, Jabbar WA, Sadiq AS, Patel H. Deep learning-based classification model for botnet attack detection. J Ambient Intell Human Comput. 2020:1–10.

  46. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J et al (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024. https://doi.org/10.1109/TMI.2014.2377694

    Article  Google Scholar 

  47. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS et al (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117. https://doi.org/10.1038/sdata.2017.117

    Article  Google Scholar 

  48. S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge." arXiv preprint arXiv:1811.02629, 2018.

  49. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection. Cancer Imaging Arch. 2017. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q.

  50. S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection. Cancer Imaging Arch. 2017. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF.

  51. Sethy PK, Behera SK (2021) A data constrained approach for brain tumour detection using fused deep features and SVM. Multimedia Tools Appl 80:28745–28760. https://doi.org/10.1007/s11042-021-11098-2

    Article  Google Scholar 

  52. Sharif MI, Li JP, Amin J, Sharif A (2021) An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network. Complex Intell Syst 7:2023–2036. https://doi.org/10.1007/s40747-021-00310-3

    Article  Google Scholar 

  53. Sunsuhi GS, Jose SA (2022) An Adaptive Eroded Deep Convolutional neural network for brain image segmentation and classification using Inception ResnetV2. Biomed Signal Proc Control 78:103863

    Article  Google Scholar 

  54. Sachdeva J, Sharma D, Ahuja CK. Comparative Analysis of Different Deep Convolutional Neural Network Architectures for Classification of Brain Tumor on Magnetic Resonance Images. Arch Computational Methods Eng. (2024):1–20.

  55. Vatanpour M, Haddadnia J (2024) Brain tumour segmentation of MR images based on custom attention mechanism with transfer-learning. IET Image Proc 18(4):886–896

    Article  Google Scholar 

  56. Mayer R, Simone CB, Turkbey B, Choyke P (2021) Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement. Quant Imaging Med Surg 11(1):119

    Article  Google Scholar 

  57. Cheng, J. Brain tumor dataset. Figshare. Dataset. Accessed: Sep. 19, 2019. [Online]. Available: https://doi.org/10.6084/m9.figshare.1512427.v5.

  58. Roy Choudhury A, Vanguri R, Jambawalikar SR, Kumar P. Segmentation of brain tumors using DeepLabv3+. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, pp. 154–167. Springer International Publishing, 2019.

  59. Zhao X, Yihong Wu, Song G, Li Z, Zhang Y, Fan Y (2018) A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 43:98–111

    Article  Google Scholar 

  60. Zhang Y, Zhong P, Jie D, Jiewei Wu, Zeng S, Chu J, Yilong Liu EdXWu, Tang X (2021) Brain tumor segmentation from multi-modal MR images via ensembling UNets. Frontiers in radiology 1:704888

    Article  Google Scholar 

  61. Mikhael Adriel and Pratama Gana, Br35H – Brain Tumor dataset (2020), Available at: https://www.kaggle.com/datasets/mikhaelapg/brain-tumor-detection/data.

  62. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed 157:69–84

    Article  Google Scholar 

  63. Salçin K (2019) Detection and classification of brain tumours from MRI images using faster R-CNN. Tehnički glasnik 13(4):337–342

    Article  Google Scholar 

  64. Amin J, Sharif M, Yasmin M, Fernandes SL (2020) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett. 139:118–127

    Article  Google Scholar 

  65. Kang M, Ting CM, Ting FF, Phan RC. "Bgf-yolo: Enhanced yolov8 with multiscale attentional feature fusion for brain tumor detection." arXiv preprint arXiv:2309.12585 (2023).

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Kattankulathur, Tamil Nadu, India

    Rajmohan Rajendirane

  2. Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India

    T. Ananth Kumar

  3. Department of Computer Science and Engineering, Annamalai University, Chidambaram, India

    S. G. Sandhya

  4. Department of Computer Science, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun District, Taichung City, 407224, Taiwan, R.O.C.

    Yu-Chen Hu

Authors
  1. Rajmohan Rajendirane
    View author publications

    Search author on:PubMed Google Scholar

  2. T. Ananth Kumar
    View author publications

    Search author on:PubMed Google Scholar

  3. S. G. Sandhya
    View author publications

    Search author on:PubMed Google Scholar

  4. Yu-Chen Hu
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Yu-Chen Hu.

Ethics declarations

Competing interests

The authors declare that they do not have any conflict of interests that influence the work reported in this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajendirane, R., Ananth Kumar, T., Sandhya, S.G. et al. A novel brain tumor segmentation and classification model using deep neural network over MRI-flair images. Multimed Tools Appl 84, 17645–17676 (2025). https://doi.org/10.1007/s11042-024-19487-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1007/s11042-024-19487-z

Keywords

Profiles

  1. T. Ananth Kumar View author profile
  2. Yu-Chen Hu View author profile