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Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification

  • 1218: Engineering Tools and Applications in Medical Imaging
  • Published:
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Abstract

Computer-aided diagnosis (CAD) system may be utilized as assistants for doctors and radiologists for the detection of disease. CAD systems using deep learning approaches are promising in diagnosing brain tumors but due to their computationally intensive nature, they are resilient to deploy in real-time scenarios where speed, as well as accuracy, is required. Further, it is necessary for the deep-learning models to capture multi-scale information as the task brain-tumor classification requires modelling pixel-to-pixel relationship and spatial-contexts in tumor-affected regions. To this end, this paper introduces the representational feature learning powers of deep, efficient and lighter deep learning architectures based on novel weight initialization and layers freezing for brain tumor classification. We use five different weight initialization and freezing configurations, four from the domain of transfer learning and the remaining being random initialization. These configurations are applied over different parameters and memory efficient architectures. Results suggest that when architecture is initiated adequately with correct weight initialization configuration based on the number of trainable parameters and architectural depth, performance obtained is optimal. Experimentation over eight different CNN architectures and five different weight initialization configurations was conducted and therefore training and evaluation of 40 deep learning frameworks was carried out. From the comprehensive experimental analyses of classification performances over three classes of brain tumor, it is evident that DenseNet201 based transfer learning model with initial 5 convolution layers frozen attains state-of-the-art accuracy of 98.22% while the lightweight models of MobileNet outperform many other models attaining the highest 97.87% accuracy for transfer learning configuration with initial 3 convolutional layers frozen while sizing only 42.6 MBs. The DenseNet201 model utilizes densely-flowing skip connections which in-turn allows the model to utilize the features learning from different spatial-contexts to formulate understanding of features in accordance with current receptive fields. With the 5 convolutional layer frozen transfer-learning scheme the same architecture achieves a performance gain of 0.88% over the state-of-art-methods. Further, the efficacy of the random initialization paradigm for brain tumor classification is investigated, results suggest that the random initialization framework can be promising if the number of trainable parameters is kept in accordance with training data quantities.

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References

  1. 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, Singapore, pp 183–189

    Chapter  Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. Buetow PC, Smirniotopoulos JG, Done S (1990) Congenital brain tumors: a review of 45 cases. AJR Am J Roentgenol 155(3):587–593

    Article  Google Scholar 

  4. Cascio D, Taormina V, Raso G (2019) Deep CNN for IIF images classification in autoimmune diagnostics. Appl Sci 9(8):1618

    Article  Google Scholar 

  5. Cheng J (2017) Brain tumor dataset (version 5). Figshare. Retrieved 16 November 2020 from https://doi.org/10.6084/m9.figshare.1512427.v5

  6. 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

    Article  Google Scholar 

  7. Cheplygina V, de Bruijne M, Pluim JP (2019) Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal 54:280–296

    Article  Google Scholar 

  8. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258

    Google Scholar 

  9. Deepak S, Ameer PM (2019) Brain tumor classification using deep CNN features via transfer learning. Computerized Medical Imaging and Graphics 111:103345

  10. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

    Chapter  Google Scholar 

  11. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society 31(4–5):198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002

    Article  Google Scholar 

  12. Dong Y, Jiang Z, Shen H, Pan WD, Williams LA, Reddy VV, Benjamin W, Bryan AW (2017) Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells. In: 2017 IEEE EMBS international conference on biomedical & health informatics (BHI). IEEE, pp 101–104

    Chapter  Google Scholar 

  13. Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37(2):505–515

    Article  Google Scholar 

  14. Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678

    Article  Google Scholar 

  15. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256

    Google Scholar 

  16. 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

    Article  Google Scholar 

  17. Harvard Medical School, http://med.harvard.edu/AANLIB/

  18. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: European conference on computer vision. Springer, Cham, pp 630–645

    Google Scholar 

  19. Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowledge-Based Syst 6(02):107–116

    Article  Google Scholar 

  20. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  21. 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

    Google Scholar 

  22. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. PMLR 448–456

  23. 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

    Google Scholar 

  24. Kaur T, Gandhi TK (2020) Deep convolutional neural networks with transfer learning for automated brain image classification. Mach Vis Appl 31(3):1–16

    Article  Google Scholar 

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

  26. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 25:1097–1105

    Google Scholar 

  27. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  28. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  29. Mehrotra R, Ansari MA, Agrawal R, Anand RS (2020) A transfer learning approach for AI-based classification of brain tumors. Mach Learn Appl 2:100003

    Google Scholar 

  30. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML

  31. Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188–197

    Article  Google Scholar 

  32. Raghu M, Zhang C, Kleinberg J, Bengio S (2019) Transfusion: understanding transfer learning for medical imaging. In: Advances in neural information processing systems, pp 3347–3357

    Google Scholar 

  33. Ranjan A, Singh VP, Mishra RB, Thakur AK, Singh AK (2021) Sentence polarity detection using stepwise greedy correlation based feature selection and random forests: an fMRI study. Journal of Neurolinguistics 59:100985

  34. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  Google Scholar 

  35. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  36. Surawicz TS, McCarthy BJ, Kupelian V, Jukich PJ, Bruner JM, Davis FG (1999) Descriptive epidemiology of primary brain and CNS tumors: results from the central brain tumor registry of the United States, 1990-1994. Neuro-oncology 1(1):14–25

    Google Scholar 

  37. 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 

  38. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

    Google Scholar 

  39. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:1602.07261

  40. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

    Google Scholar 

  41. Ting DSW, Cheung CYL, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, … Wong TY (2017) Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Jama 318(22):2211–2223

  42. 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

    Article  Google Scholar 

  43. Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q (2020) IMCFN: image-based malware classification using fine-tuned convolutional neural network architecture. Comput Netw 171:107138

    Article  Google Scholar 

  44. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

    Google Scholar 

  45. Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC (2010) Machine learning in medical imaging. IEEE Signal Process Mag 27(4):25–38

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Raipur, Raipur, India

    Aman Verma

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Vibhav Prakash Singh

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  1. Aman Verma
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Correspondence to Vibhav Prakash Singh.

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Verma, A., Singh, V.P. Design, analysis and implementation of efficient deep learning frameworks for brain tumor classification. Multimed Tools Appl 81, 37541–37567 (2022). https://doi.org/10.1007/s11042-022-13545-0

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