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A novel multi-class brain tumor classification method based on unsupervised PCANet features

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Abstract

Brain tumors are one of the most severe tumors in the human body because of their nonlinear morphological and textural characteristics. The appropriate clinical practices selected by the surgeons for the patients can be improved by using automated brain tumor diagnosis systems based on magnetic resonance images (MRIs). Therefore, improving the efficiency of these systems plays an essential role in saving the lives of patients. In this paper, a novel multi-class brain tumor classification method is proposed. The proposed method comprises two modules: a simple unsupervised convolutional PCANet module is utilized for feature extraction and a supervised CNN module for feature classification. We modified the PCANet model by applying a nonlinear activation function on the convolutional feature maps. In addition, the learned convolutional PCA filters are computed across the 3D feature maps contrary to the traditional PCANet model, which apply PCA filters on the 2D maps. These modifications enhance the features’ expressive power compared with the traditional PCANet and the deep CNN models. The unsupervised features extracted from the modified PCANet module are followed by a simple CNN classification module. The unsupervised PCANet does not require a large number of training data compared with its competitive supervised CNN. We apply grid-search optimization to obtain the optimal hyper-parameters for the classification module. Four public benchmark datasets containing 9581 MRI brain images are utilized to evaluate the performance of the proposed method. The datasets contain different classification tasks, number of samples, image sizes, contrast, and planes. Our proposed method achieved the highest performance compared with other state-of-the-art methods. The proposed method encourages medical imaging diagnosis and can solve the size limitation problem in medical datasets.

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Data availability

The data that support the findings of this study are available from: Brats-Small-2c-dataset: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection Brats-large-2cdataset: https://www.kaggle.com/ahmedhamada0/brain-tumor-detection Brats-large- 4c-dataset: https://www.pitt.edu/∼emotion/ck-spread.htm Cheng-dataset: https://figshare.com/articles/dataset/braintumordataset/1512427.

References

  1. Louis DN, Perry A, Reifenberger G, Von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta neuropathologica. 131(6):803–820

    Google Scholar 

  2. Yang Y, Yan LF, Zhang X, Han Y, Nan HY, Hu YC et al (2018) Glioma grading on conventional MR images: a deep learning study with transfer learning. Front Neurosci 12:804

    Google Scholar 

  3. Chahal PK, Pandey S, Goel S (2020) A survey on brain tumor detection techniques for MR images. Multimed Tools Appl 79(29):21771–21814

    Google Scholar 

  4. El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert syst Appl 41(11):5526–5545

    Google Scholar 

  5. Gunasekara SR, Kaldera H, Dissanayake MB (2021) A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. J Healthc Eng 2021:1–13

    Google Scholar 

  6. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    Google Scholar 

  7. 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

    Google Scholar 

  8. Mohan G, Subashini MM (2018) MRI based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 39:139–161

    Google Scholar 

  9. 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 Syst 32(2):507–522

    Google Scholar 

  10. Biratu ES, Schwenker F, Ayano YM, Debelee TG (2021) A survey of brain tumor segmentation and classification algorithms. J Imaging 7(9):179

    Google Scholar 

  11. Nazir M, Shakil S, Khurshid K (2021) Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Comput Med Imaging Graph 91:101940

    Google Scholar 

  12. Tiwari A, Srivastava S, Pant M (2020) Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recognit Lett 131:244–260

    Google Scholar 

  13. Amin KM, Shahin A, Guo Y (2016) A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81:210–220

    Google Scholar 

  14. Hemanth DJ, Anitha J, Naaji A, Geman O, Popescu DE et al (2018) A modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7:4275–4283

    Google Scholar 

  15. Paul JS, Plassard AJ, Landman BA, Fabbri D (2017) Deep learning for brain tumor classification. In: Medical imaging 2017: biomedical applications in molecular, structural, and functional imaging. vol 10137. SPIE, pp 253–268

  16. 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 and signal processing (ICASSP). IEEE, pp 1368–1372

  17. Shahin AI, Guo Y, Amin KM, Sharawi AA (2019) White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Programs Biomed 168:69–80

    Google Scholar 

  18. Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z et al (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one 10(10):e0140381

    Google Scholar 

  19. Öksüz C, Urhan O, Güllü MK (2022) Brain tumor classification using the fused features extracted from expanded tumor region. Biomed Signal Process Control 72:103356

    Google Scholar 

  20. Verma A, Singh VP (2022) Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification. Multimed Tools Appl 81(26):37541–37567

    Google Scholar 

  21. Saurav S, Sharma A, Saini R, Singh S (2023) An attention-guided convolutional neural network for automated classification of brain tumor from MRI. Neural Comput Appl 35(3):2541–2560

    Google Scholar 

  22. Sasank V, Venkateswarlu S (2022) Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images. Multimed Tools Appl 81(6):8021–8057

    Google Scholar 

  23. Shahin AI, Aly W, Aly S (2022) MBTFCN: a novel modular fully convolutional network for MRI brain tumor multi-classification. Expert Syst Appl 212:118776

    Google Scholar 

  24. Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032

    MATH  MathSciNet  Google Scholar 

  25. Low CY, Teoh ABJ, Toh KA (2017) Stacking PCANet+: an overly simplified convnets baseline for face recognition. IEEE Signal Process Lett 24(11):1581–1585

    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). IEEE, pp 0252–0257

  27. Tahir B, Iqbal S, Usman Ghani Khan M, Saba T, Mehmood Z, Anjum A et al (2019) Feature enhancement framework for brain tumor segmentation and classification. Microsc Res Tech 82(6):803–811

    Google Scholar 

  28. Mohsen H, El-Dahshan ESA, El-Horbaty ESM, Salem ABM (2018) Classification using deep learning neural networks for brain tumors. Future Comput Inform J 3(1):68–71

    Google Scholar 

  29. Afshar P, Mohammadi A, Plataniotis KN (2018) Brain tumor type classification via capsule networks. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 3129–3133

  30. 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. Springer, pp 183–189

  31. 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

    Google Scholar 

  32. Kang Y, Choi SH, Kim YJ, Kim KG, Sohn CH, Kim JH et al (2011) Gliomas: histogram analysis of apparent diffusion coefficient maps with standard-or high-b-value diffusion-weighted MR imaging-correlation with tumor grade. Radiology 261(3):882–890

    Google Scholar 

  33. Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y et al (2017) Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 27(8):3509–3522

    Google Scholar 

  34. Deepak S, Ameer P (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345

    Google Scholar 

  35. 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

    Google Scholar 

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

    Google Scholar 

  37. 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

    Google Scholar 

  38. Çinarer G, Emiroğlu BG, Yurttakal AH (2020) Prediction of glioma grades using deep learning with wavelet radiomic features. Appl Sci 10(18):6296

    Google Scholar 

  39. Irmak E (2021) Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Trans Electr Eng 45(3):1015–1036

    Google Scholar 

  40. Huang Z, Zhu X, Ding M, Zhang X (2020) Medical image classification using a light-weighted hybrid neural network based on PCANet and DenseNet. IEEE Access 8:24697–24712

    Google Scholar 

  41. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  42. Aly S, Mohamed A (2019) Unknown-length handwritten numeral string recognition using cascade of PCA-SVMNet classifiers. IEEE Access 7:52024–52034

    Google Scholar 

  43. Aly W, Aly S, Almotairi S (2019) User-independent American sign language alphabet recognition based on depth image and PCANet features. IEEE Access 7:123138–123150

    Google Scholar 

  44. Abdelbaky A, Aly S (2020) Human action recognition using short-time motion energy template images and PCANet features. Neural Comput Appl 32(16):12561–12574

    Google Scholar 

  45. JLB DPK (2015) Adam: a method for stochastic optimization. In: 3rd international conference for learning representations, San Diego

  46. : Brats-Small-2c-dataset. Accessed 30 Sep 2021. https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection

  47. : Brats-large-2c-dataset. Accessed 30 Sep 2021. https://www.kaggle.com/ahmedhamada0/brain-tumor-detection

  48. : Brats-large-4c-dataset. Accessed 30 Sep 2021. https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri

  49. : Cheng-dataset. Accessed 30 Sep 2021. https://figshare.com/articles/dataset/brain_tumor_dataset/1512427

  50. Kaplan K, Kaya Y, Kuncan M, Ertunç HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses 139:109696

    Google Scholar 

  51. Kalaiselvi T, Padmapriya S, Sriramakrishnan P, Somasundaram K (2020) Deriving tumor detection models using convolutional neural networks from MRI of human brain scans. Int J Inf Technol 12(2):403–408

    Google Scholar 

  52. Amin J, Sharif M, Gul N, Raza M, Anjum MA, Nisar MW et al (2020) Brain tumor detection by using stacked autoencoders in deep learning. J Med Syst 44(2):1–12

    Google Scholar 

  53. Demir F, Akbulut Y (2022) A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification. Biomed Signal Process Control 75:103625

    Google Scholar 

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Acknowledgements

Saleh Aly would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project No. R-2023-8.

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Authors and Affiliations

  1. Department of Natural and Applied Sciences, Community College, Majmaah University, 11952, Al-Majmaah, Saudi Arabia

    Ahmed I. Shahin

  2. Department of Information Technology, College of Computer and Information Sciences, Majmaah University, 11952, Al-Majmaah, Saudi Arabia

    Saleh Aly & Walaa Aly

  3. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Saleh Aly & Walaa Aly

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  1. Ahmed I. Shahin
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Correspondence to Ahmed I. Shahin or Walaa Aly.

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Shahin, A.I., Aly, S. & Aly, W. A novel multi-class brain tumor classification method based on unsupervised PCANet features. Neural Comput & Applic 35, 11043–11059 (2023). https://doi.org/10.1007/s00521-023-08281-x

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