Skip to main content
SpringerLink
Log in

Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture

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

Abstract

Detecting and classifying a brain tumor is a challenge that consumes a radiologist’s time and effort while requiring professional expertise. To resolve this, deep learning techniques can be used to help automate the process. The aim of this paper is to enhance the accuracy of brain tumor classification using a new layered architecture of deep neural networks rather than the current state-of-the-art algorithms. In this paper, we propose automated tumor classification by concatenating two convolutional neural network structures of layers and tuning the hyperparameters by utilizing Bayesian optimization. The proposed solution focuses on enhancing the accuracy of classifying tumors to increase the level of trust in the technologies employed in the medical field. The work is tested and evaluated to predict the classification of magnetic resonance imaging inputs and achieving a higher accuracy (97.37%) than other similar works, with accuracies between 84.19% and 96.13%, for the same dataset.

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

Similar content being viewed by others

Abbreviations

MRI:

Magnetic Resonance Imaging

GPU:

Graphical Processing Unit

CNN:

Convolutional Neural Network

ELOBA_λ:

Epochs, Learning rate, Optimizer, Batch size

VGG16:

Visual Geometry Group CNN 16 Layers Deep

VGG19:

Visual Geometry Group CNN 19 Layers Deep

ResNet50:

Residual Network 50 Layers Deep

ECOC-SVM:

Error Correcting Output Coding Support Vector Machine

R-CNN:

Region-based Convolutional Neural Networks

PCA:

Principal Component Analysis

NGIST:

Normalized GIST

PCA- NGIST:

Principal Component Analysis Normalized GIST

RELM:

Recurrent Extreme Learning Machine

2D:

Two-Dimensional

GLCM:

Gray-Level Co-occurrence Matrix

LBP:

Local Binary Pattern

RF:

Random Forest

RF-PCA:

Random Forest Principal Component Analysis

CE-MRI:

Contrast-Enhanced Magnetic Resonance Imaging

KELM:

Kernel Extreme Learning Machine

SVM:

Support Vector Machine

RBF:

Radial Base Function

ReLU:

Rectified Linear Unit

KE-CNN:

Kernel Extreme Convolution Neural Network

DCGAN:

Deep Convolutional Generative Adversarial Network

HOG:

Histogram of Oriented Gradients

SURF:

Speeded Up Robust Features

TP:

True Positives

TN:

True Negatives

FP:

False Positives

FN:

False Negatives

DWT:

Discrete Wavelet Transform

CapsNet:

Capsule Neural Network

References

  1. Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF (2018, 2018) Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process (1):97

  2. Abiwinanda N, Hanif M, Hesaputra ST, Handayani A, Mengko TR (2019) Brain tumor classification using convolutional neural network, In World Congress on Medical Physics and Biomedical Engineering 2018, pp. 183–189

  3. Afshar P, Plataniotis KN, Mohammadi A (2019) Capsule Networks for Brain Tumor Classification Based on Mri Images and Coarse Tumor Boundaries, In ICASSP 2019-2019 IEEE international conference on acoustics, Speech Sig Process (ICASSP), pp. 1368–1372

  4. Akram MU, Usman A (2011) Computer aided system for brain tumor detection and segmentation, In International Conference on Computer Networks and Information Technology, pp. 299–302

  5. Alshayeji MH, Al-Rousan MA, Ellethy H, Abed S (2018) An efficient multiple sclerosis segmentation and detection system using neural networks. Comput Electr Eng 71:191–205. https://doi.org/10.1016/j.compeleceng.2018.07.020

    Article  Google Scholar 

  6. Anaraki AK, Ayati M, Kazemi F (2019) Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 39(1):63–74

    Article  Google Scholar 

  7. Araújo T et al (2017) Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6):e0177544

    Article  Google Scholar 

  8. Bankman I (2008) Handbook of medical image processing and analysis. Elsevier

  9. Bauer S, Wiest R, Nolte L-P, Reyes M (2013) A survey of MRI-based medical image analysis for brain tumor studies. Phys Med Biol 58(13):R97–R129

    Article  Google Scholar 

  10. Carneiro T, Da Nóbrega RVM, Nepomuceno T, Bian G-B, De Albuquerque VHC, Reboucas Filho PP (2018) Performance analysis of Google Colaboratory as a tool for accelerating deep learning applications. IEEE Access 6:61677–61685

    Article  Google Scholar 

  11. Chen X, Hao Liew J, Xiong W, Chui C-K, Ong S-H (2018) Focus, segment and erase: an efficient network for multi-label brain tumor segmentation, In Proceedings of the European Conference on Computer Vision (ECCV), pp. 654–669

  12. Cheng J (2017) Brain tumor dataset, Figshare Dataset

  13. DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123

    Article  Google Scholar 

  14. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database, In 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  15. 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 

  16. 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), pp. 0252–0257

  17. Jannesari M et al. (2018) Breast Cancer Histopathological Image Classification: A Deep Learning Approach, In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2405–2412

  18. Karako K, Chen Y, Tang W (2018) On medical application of neural networks trained with various types of data. Biosci Trends 12(6):553–559

    Article  Google Scholar 

  19. Ker J, Wang L, Rao J, Lim T (2017) Deep learning applications in medical image analysis. Ieee Access 6:9375–9389

    Article  Google Scholar 

  20. Laws ER, Thapar K (1993) Brain tumors. CA Cancer J Clin 43(5):263–271. https://doi.org/10.3322/canjclin.43.5.263

    Article  Google Scholar 

  21. Liu P, Yu H, Cang S (2019) Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dyn 98(2):1447–1464. https://doi.org/10.1007/s11071-019-05170-8

    Article  Google Scholar 

  22. Maier A, Syben C, Lasser T, Riess C (2019) A gentle introduction to deep learning in medical image processing. Z Für Med Phys 29(2):86–101

    Article  Google Scholar 

  23. Mehra R (2018) Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 4(4):247–254

    Article  Google Scholar 

  24. Mzoughi H, Njeh I, Wali A, Slima MB, BenHamida A, Mhiri C, Mahfoudhe KB (Aug. 2020) Deep multi-scale 3D convolutional neural network (CNN) for MRI Gliomas brain tumor classification. J Digit Imaging 33(4):903–915. https://doi.org/10.1007/s10278-020-00347-9

    Article  Google Scholar 

  25. Othman MF, Basri MAM (2011) Probabilistic neural network for brain tumor classification, In 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, pp. 136–138

  26. Papanastasopoulos Z et al. (2020) Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI, p. 52, doi: https://doi.org/10.1117/12.2549298.

  27. Pashaei A, Sajedi H, Jazayeri N (2018) Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines In 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314–319

  28. Pelikan M, Goldberg DE, Cantú-Paz E (1999) BOA: The Bayesian optimization algorithm, In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1, pp. 525–532

  29. Radiopaedia (2015) Radiopaedia. org

  30. 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

    Article  Google Scholar 

  31. Saouli R, Akil M, Kachouri R (2018) Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput Methods Prog Biomed 166:39–49

    Article  Google Scholar 

  32. Saraswathi V, Gupta D (2019) Classification of Brain Tumor using PCA-RF in MR Neurological Images, In 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp. 440–443

  33. Schiff GD (2008) Minimizing diagnostic error: the importance of follow-up and feedback. Am J Med 121(5):S38–S42

    Article  Google Scholar 

  34. Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Proces Syst 25:2951–2959

    Google Scholar 

  35. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks, in 2016 international joint conference on neural networks (IJCNN), pp. 2560–2567

  36. Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215–69225

  37. Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2019) A novel weakly-supervised approach for RGB-D-based nuclear waste object detection. IEEE Sensors J 19(9):3487–3500. https://doi.org/10.1109/JSEN.2018.2888815

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. Tang Z, Li C, Wu J, Liu P, Cheng S (Aug. 2019) Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI. Front Inf Technol Electron Eng 20(8):1087–1098. https://doi.org/10.1631/FITEE.1800083

    Article  Google Scholar 

  40. Tang Z, Yu H, Lu C, Liu P, Jin X (2019) Single-trial classification of different movements on one arm based on ERD/ERS and Corticomuscular coherence. IEEE Access 7:128185–128197. https://doi.org/10.1109/ACCESS.2019.2940034

    Article  Google Scholar 

  41. Wang G, Kang W, Wu Q, Wang Z, Gao J (2018) Generative Adversarial Network (GAN) Based Data Augmentation for Palmprint Recognition, In 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–7

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, P.O. Box 5969, Kuwait City, 13060, Safat, Kuwait

    Mohammad Alshayeji, Jassim Al-Buloushi, Ali Ashkanani & Sa’ed Abed

Authors
  1. Mohammad Alshayeji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jassim Al-Buloushi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Ashkanani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sa’ed Abed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Alshayeji.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alshayeji, M., Al-Buloushi, J., Ashkanani, A. et al. Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture. Multimed Tools Appl 80, 28897–28917 (2021). https://doi.org/10.1007/s11042-021-10927-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-10927-8

Keywords

Search

Navigation