Skip to main content
SpringerLink
Log in

An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Multi-class brain tumor classification is an important area of research in the field of medical imaging because of the different tumor characteristics. One such challenging problem is the multiclass classification of brain tumors using MR images. Since accuracy is critical in classification, computer vision researchers are introducing a number of techniques; However, achieving high accuracy remains challenging when classifying brain images. Early diagnosis of brain tumor types can activate timely treatment, thereby improving the patient’s chances of survival. In recent years, deep learning models have achieved promising results, especially in classifying brain tumors to help neurologists. This work proposes a deep transfer learning model that accelerates brain tumor detection using MR imaging. In this paper, five popular deep learning architectures are utilized to develop a system for diagnosing brain tumors. The architectures used is this paper are Xception, DenseNet201, DenseNet121, ResNet152V2, and InceptionResNetV2. The final layer of these architectures has been modified with our deep dense block and softmax layer as the output layer to improve the classification accuracy. This article presents two main experiments to assess the effectiveness of the proposed model. First, three-class results using images from patients with glioma, meningioma, and pituitary are discussed. Second, the results of four classes are discussed using images of glioma, meningioma, pituitary and healthy patients. The results show that the proposed model based on Xception architecture is the most suitable deep learning model for detecting brain tumors. It achieves a classification accuracy of 99.67% on the 3-class dataset and 95.87% on the 4-class dataset, which is better than the state-of-the-art methods. In conclusion, the proposed model can provide radiologists with an automated medical diagnostic system to make fast and accurate decisions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Aderghal K, Afdel K, Benois-Pineau J, Catheline G, Alzheimer's Disease Neuroimaging Initiative (2020) Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities. Heliyon 6(12):e05652

    Article  Google Scholar 

  2. Alanazi MF, Ali MU, Hussain SJ, Zafar A, Mohatram M, Irfan M, AlRuwaili R, Alruwaili M, Ali NH, Albarrak AM (2022) Brain tumor/mass classification framework using magnetic-resonance-imaging-based isolated and developed transfer deep-learning model. Sensors 22(1):372. https://doi.org/10.3390/s22010372

    Article  Google Scholar 

  3. 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. https://doi.org/10.1016/j.patrec.2017.10.036

    Article  Google Scholar 

  4. Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics Biomed Eng 39(1):63–74. https://doi.org/10.1016/j.bbe.2018.10.004

    Article  Google Scholar 

  5. Ansari M, Mehrotra R, Agrawal R (2020) Detection and classification of brain tumor in MRI images using wavelet transform and support vector machine. J Interdiscipl Math 23(5):955–966. https://doi.org/10.1080/09720502.2020.1723921

    Article  Google Scholar 

  6. Badža MM, Barjaktarović MČ (2020) Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 10(6):1999. https://doi.org/10.3390/app10061999

    Article  Google Scholar 

  7. Bangare SL, Pradeepini G, Patil ST (2017) Brain tumor classification using mixed method approach. In: 2017 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, pp 1–4. https://doi.org/10.1109/ICICES.2017.8070748

  8. Bengio Y (2012) Practical recommendations for gradient-based training of deep architectures, In Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg, pp 437–478

    Book  Google Scholar 

  9. Bhuvaji S, Kadam A, Bhumkar P, Dedge S, Kanchan S (n.d.) Brain tumor classification (MRI) dataset. Available: https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri. Accessed 5 Aug 2021

  10. Chauhan S, More A, Uikey R, Malviya P, Moghe A (2017) Brain tumor detection and classification in MRI images using image and data mining. In: 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), Bhopal, pp 223–231. https://doi.org/10.1109/RISE.2017.8378158

  11. 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(10):e0140381. https://doi.org/10.1371/journal.pone.0140381

    Article  Google Scholar 

  12. Cheng J, Yang W, Huang M (2017) Brain Tumor Dataset. https://doi.org/10.6084/m9.figshare. 2017;1512427:v5

  13. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195

  14. Citak-Er F, Firat Z, Kovanlikaya I, Ture U, Ozturk-Isik E (2018) Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T. Comput Biol Med 99:154–160. https://doi.org/10.1016/j.compbiomed.2018.06.009

    Article  Google Scholar 

  15. Cortes, C, Mohri, M, Rostamizadeh, A (2012) L2 regularization for learning kernels. arXiv preprint arXiv:1205.2653

  16. Deepa SN, Aruna Devi B (2011) Neural networks and SMO based classification for brain tumor. In: 2011 World Congress on Information and Communication Technologies, Mumbai, pp 1032–1037. https://doi.org/10.1109/WICT.2011.6141390

  17. Deepak S, Ameer P (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345. https://doi.org/10.1016/j.compbiomed.2019.103345

    Article  Google Scholar 

  18. Devi T Menaka, GR, Arockiaraj SX (2018) MR brain tumor classification and segmentation via wavelets. In: 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp 1–4. https://doi.org/10.1109/WiSPNET.2018.8538643

  19. 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. https://doi.org/10.3390/healthcare9020153

    Article  Google Scholar 

  20. Dunford R, Su Q, Tamang E (2014) The Pareto Principle. Plymouth Student Sci 7(1):140–148

    Google Scholar 

  21. Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273. https://doi.org/10.1109/access.2019.2904145

    Article  Google Scholar 

  22. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  23. Hemanth DJ, Anitha J, Naaji A, Geman O, Popescu DE (2018) A modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7:4275–4283. https://doi.org/10.1109/access.2018.2885639

    Article  Google Scholar 

  24. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243

  25. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Int Conf Mach Learn PMLR 37:448–456

    Google Scholar 

  26. Ismael MR, Abdel-Qader I (2018) Brain tumor classification via statistical features and back-propagation neural network. In: 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, pp 0252–0257. https://doi.org/10.1109/EIT.2018.8500308

  27. Ismael SAA, Mohammed A, Hefny H (2020) An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artif Intell Med 102:101779

    Article  Google Scholar 

  28. Kalaiselvi T, Padmapriya ST (2021) Brain tumor diagnostic system—a deep learning application. Machine Vision Inspection Systems, Volume 2. Mach Learn Based Approaches 27:69–90

    Google Scholar 

  29. Kang J, Ullah Z, Gwak J (2021) MRI-based brain tumor classification using Ensemble of Deep Features and Machine Learning Classifiers. Sensors 21(6):2222. https://doi.org/10.3390/s21062222

    Article  Google Scholar 

  30. Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RS (2020) Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recogn Lett 129:293–303. https://doi.org/10.1016/j.patrec.2019.11.034

    Article  Google Scholar 

  31. Khan MA, Sarfraz MS, Alhaisoni M, Albesher AA, Wang S, Ashraf I (2020) StomachNet: optimal deep learning features fusion for stomach abnormalities classification. IEEE Access 8:197969–197981. https://doi.org/10.1109/access.2020.3034217

    Article  Google Scholar 

  32. Khan MA, Rubab S, Kashif A, Sharif MI, Muhammad N, Shah JH, Zhang Y-D, Satapathy SC (2020) Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recogn Lett 129:77–85. https://doi.org/10.1016/j.patrec.2019.11.014

    Article  Google Scholar 

  33. Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS (2018) Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl Sci 8(1):27. https://doi.org/10.3390/app8010027

    Article  Google Scholar 

  34. Kingma, DP, Ba, J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  35. Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A (2016) Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. Neuroimage 129:460–469. https://doi.org/10.1016/j.neuroimage.2016.01.024

    Article  Google Scholar 

  36. Kokkalla S, Kakarla J, Venkateswarlu IB, Singh M (2021) Three-class brain tumor classification using deep dense inception residual network. Soft Comput 25(13):8721–8729. https://doi.org/10.1007/s00500-021-05748-8

    Article  Google Scholar 

  37. Kumar PMS, Chatteijee S (2016) Computer aided diagnostic for cancer detection using MRI images of brain (Brain tumor detection and classification system). In: 2016 IEEE Annual India Conference (INDICON), Bangalore, pp 1–6. https://doi.org/10.1109/INDICON.2016.7838875

  38. Latif G, Butt, MM, Khan, AH, Butt, O, Iskandar, DA (2017) Multiclass brain Glioma tumor classification using block-based 3D Wavelet features of MR images. In: 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, pp 333–337. https://doi.org/10.1109/ICEEE2.2017.7935845

  39. Lavanyadevi R, Machakowsalya M, Nivethitha J, Niranjil Kumar A (2017) Brain tumor classification and segmentation in MRI images using PNN. In: 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, pp 1–6. https://doi.org/10.1109/ICEICE.2017.8191888

  40. Lin, M, Chen, Q, Yan, S (2013) Network in network. arXiv preprint arXiv:1312.4400

  41. Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820. https://doi.org/10.1007/s00401-016-1545-1

    Article  Google Scholar 

  42. Maharjan S, Alsadoon A, Prasad P, Al-Dalain T, Alsadoon OH (2020) A novel enhanced softmax loss function for brain tumour detection using deep learning. J Neurosci Methods 330:108520. https://doi.org/10.1016/j.jneumeth.2019.108520

    Article  Google Scholar 

  43. Mathew AR, Anto PB (2017) Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, pp 75–78. https://doi.org/10.1109/CSPC.2017.8305810

  44. Mehrotra R, Ansari M, Agrawal R, Anand R (2020) A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl 2:100003. https://doi.org/10.1016/j.mlwa.2020.100003

    Article  Google Scholar 

  45. Minz A, Mahobiya C (2017) MR image classification using adaboost for brain tumor type. In: 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, pp 701–705. https://doi.org/10.1109/IACC.2017.0146

  46. Muhammad K, Khan S, Del Ser J, de Albuquerque VHC (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn 32(2):507–522. https://doi.org/10.1109/tnnls.2020.2995800

    Article  Google Scholar 

  47. Naseer A, Yasir T, Azhar A, Shakeel T, Zafar K (2021) Computer-aided brain tumor diagnosis: performance evaluation of deep learner CNN using augmented brain MRI. Int J Biomed Imaging 5513500. https://doi.org/10.1155/2021/5513500

  48. Pashaei A, Ghatee M, Sajedi H (2020) Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images. J Real-Time Image Proc 17(4):1051–1066. https://doi.org/10.1007/s11554-019-00852-3

    Article  Google Scholar 

  49. Polat, Ö, Güngen, C (2021) Classification of brain tumors from MR images using deep transfer learning. J Supercomput, 1–17. https://doi.org/10.1007/s11227-020-03572-9

  50. Polly FP, Shil SK, Hossain MA, Ayman A, Jang YM (2018) Detection and classification of HGG and LGG brain tumor using machine learning. In: 2018 International Conference on Information Networking (ICOIN), Chiang Mai, pp 813–817. https://doi.org/10.1109/ICOIN.2018.8343231

  51. Qiu Y, Yan S, Gundreddy RR, Wang Y, Cheng S, Liu H, Zheng B (2017) A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J X-ray Scie Technol 25(5):751–763. https://doi.org/10.3233/xst-16226

    Article  Google Scholar 

  52. Rajan P, Sundar C (2019) Brain tumor detection and segmentation by intensity adjustment. J Med Syst 43(8):1–13. https://doi.org/10.1007/s10916-019-1368-4

    Article  Google Scholar 

  53. Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ Syst Signal Process 39(2):757–775. https://doi.org/10.1007/s00034-019-01246-3

    Article  Google Scholar 

  54. Rehman A, Khan MA, Saba T, Mehmood Z, Tariq U, Ayesha N (2021) Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc Res Tech 84(1):133–149. https://doi.org/10.1002/jemt.23597

    Article  Google Scholar 

  55. Ruba T, Tamilselvi R, ParisaBeham M, Aparna N (2020) Accurate classification and detection of brain cancer cells in MRI and CT images using nano contrast agents. Biomed Pharm J 13(3):1227–1237. https://doi.org/10.13005/bpj/1991

    Article  Google Scholar 

  56. Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221–230. https://doi.org/10.1016/j.cogsys.2019.09.007

    Article  Google Scholar 

  57. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW (2019) Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci 30:174–182. https://doi.org/10.1016/j.jocs.2018.12.003

    Article  Google Scholar 

  58. Salçin K (2019) Detection and classification of brain tumours from MRI images using faster R-CNN. Tehnički glasnik 13(4):337–342. https://doi.org/10.31803/tg-20190712095507

    Article  Google Scholar 

  59. Sarhan AM (2020) Brain tumor classification in magnetic resonance images using deep learning and wavelet transform. J Biomed Sci Eng 13(06):102–112. https://doi.org/10.4236/jbise.2020.136010

    Article  Google Scholar 

  60. Saxena P, Maheshwari A, Maheshwari S (2021) Predictive modeling of brain tumor: A Deep learning approach. In: Innovations in Computational Intelligence and Computer Vision, pp. 275–285. Springer, Singapore

    Google Scholar 

  61. Seetha J, Raja SS (2018) Brain tumor classification using convolutional neural networks. Biomed Pharma J 11(3):1457. https://doi.org/10.13005/bpj/1511

    Article  Google Scholar 

  62. Sharif MI, Li JP, Khan MA, Saleem MA (2020) Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recogn Lett 129:181–189. https://doi.org/10.1016/j.patrec.2019.11.019

    Article  Google Scholar 

  63. Sornam M, Kavitha MS, Shalini R (2016) Segmentation and classification of brain tumor using wavelet and Zernike based features on MRI. In: 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, pp 166–169. https://doi.org/10.1109/ICACA.2016.7887944

  64. 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

    MathSciNet  MATH  Google Scholar 

  65. Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215–69225. https://doi.org/10.1109/access.2019.2919122

    Article  Google Scholar 

  66. Suthaharan S (2016) Machine learning models and algorithms for big data classification. Integr Ser Inf Syst 36:1–12

    MathSciNet  MATH  Google Scholar 

  67. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Content-based brain tumor retrieval for MR images using transfer learning. IEEE Access 7:17809–17822. https://doi.org/10.1109/access.2019.2892455

    Article  Google Scholar 

  68. Swati ZNK, Zhao Q, Kabir M, Ali F, Ali Z, Ahmed S, Lu J (2019) Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph 75:34–46. https://doi.org/10.1016/j.compmedimag.2019.05.001

    Article  Google Scholar 

  69. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 31(1). https://doi.org/10.1609/aaai.v31i1.11231

  70. Taheri S, Gasparovic C, Shah NJ, Rosenberg GA (2011) Quantitative measurement of blood-brain barrier permeability in human using dynamic contrast-enhanced MRI with fast T1 mapping. Magn Reson Med 65(4):1036–1042. https://doi.org/10.1002/mrm.23165

    Article  Google Scholar 

  71. Toğaçar M, Ergen B, Cömert Z (2020) BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 134:109531. https://doi.org/10.1016/j.mehy.2019.109531

    Article  Google Scholar 

  72. Ullah Z, Farooq MU, Lee S-H, An D (2020) A hybrid image enhancement based brain MRI images classification technique. Med Hypotheses 143:109922. https://doi.org/10.1016/j.mehy.2020.109922

    Article  Google Scholar 

  73. Zhan T, Feng P, Hong X, Lu Z, Xiao L, Zhang Y (2017) An automatic glioma grading method based on multi-feature extraction and fusion. Technol Health Care 25(S1):377–385. https://doi.org/10.3233/thc-171341

    Article  Google Scholar 

  74. Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39(2):208–216. https://doi.org/10.3174/ajnr.a5391

    Article  Google Scholar 

Download references

Funding

This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ4757).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Sohaib Asif, Ming Zhao & Fengxiao Tang

  2. School of Mathematics, Hunan University, Changsha, China

    Yusen Zhu

Authors
  1. Sohaib Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fengxiao Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yusen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Ming Zhao or Fengxiao Tang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Asif, S., Zhao, M., Tang, F. et al. An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning. Multimed Tools Appl 82, 31709–31736 (2023). https://doi.org/10.1007/s11042-023-14828-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14828-w

Keywords

Search

Navigation