International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Predicting Brain Tumor Using Transfer Learning

  • Mustafa Abdul Salam (Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence Benha University) ;
  • Sanaa Taha (Faculty of Computers and Information, Egyptian E-Learning University ) ;
  • Sameh Alahmady (Faculty of Computers and Information, Egyptian E-Learning University ) ;
  • Alwan Mohamed (Scientific Computing Department, Faculty of Computers and Artificial Intelligence Cairo University)
  • Received : 2023.05.05
  • Published : 2023.05.30
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Abstract

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.

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References

  1. Fritz, A. et al. International Classifcation of Diseases for Oncology Vol. 3 (World Health Organization, Geneva, 2001). 
  2. Goodenberger, M. L. et al. Genetics of adult glioma. Cancer Genet. 205, 613-621 (2012).  https://doi.org/10.1016/j.cancergen.2012.10.009
  3. Claus, E. B. et al. Survival and low-grade glioma: Te emergence of genetic information. Neurosurg. Focus 38, E6 (2015). 
  4. Menze, B. H. et al. Te multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. 34, 1993-2024 (2014).  https://doi.org/10.1109/TMI.2014.2377694
  5. Mzoughi, H. et al. Deep multi-scale 3d convolutional neural network (cnn) for mri gliomas brain tumor classifcation. J. Digit. Imaging 33, 903-915 (2020).  https://doi.org/10.1007/s10278-020-00347-9
  6. Muhammad Sjjad, Salman Khan, Khan Muhammad, Wanqing Wu, Amin Ullah, Sung Wook Baik, "Multi-grade brain tumor classification using deep CNN with extensive data augmentation", Elsevier, Journal of Computational Science 30, pp 174-182, 2019.  https://doi.org/10.1016/j.jocs.2018.12.003
  7. Amin Kabir Anaraki, Moosa Ayati, Foad Kazemi, "Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms", Elsevier, Biocybergenetics and Biomedical Engineering 39, pp 63-74, 2019.  https://doi.org/10.1016/j.bbe.2018.10.004
  8. Deepak, P.M. Ameer, "Brain tumor classification using deep CNN features via transfer learning", Elsevier, Computers in Biology and Medicine 111, pp 1-7, 2019.  https://doi.org/10.1016/j.compbiomed.2019.103345
  9. R. Vimal Kurup, V. Sowmya, K. P. Soman, "Effect of Data Pre-processing on Brain Tumor Classification Using Capsulenet", Springer, ICICCT System Reliability, Quality Control, Safety, Maintenance and Management, pp 110-119, 2019. 
  10. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir, Farman Ali, Zakir Ali, Saeed Ahmed, Jianfeng Lu, "Brain tumor classification for MR images using transfer learning and finetuning", Elsevier, Computerized Medical Imaging and Graphics 75, pp 34-46, 2019.  https://doi.org/10.1016/j.compmedimag.2019.05.001
  11. Nyoman Abiwinanda, Muhammad Hanif, S. Tafwida Hesaputra, Astri Handayani, and Tati Rajab Mengko, "Brain Tumor Classification Using Convolutional Neural Network", Springer, World Congress on Medical Physics and Biomedical Engineering, pp 183-189, 2018. 
  12. F. P. Polly, S. K. Shil, M. A. Hossain, A. Ayman, and Y. M. Jang, "Detection and Classification of HGG and LGG Brain Tumor Using Machine Learning", IEEE, International Conference on Information Networking (ICOIN), 2018. 
  13. Heba Mohsen, El-Sayed A. El-Dahshan, El-Sayed M. ElHorbaty, Abdel-Badeeh M. Salem, "Classification using deep learning neural networks for brain tumors", Elsevier, Future Computing and Informatics Journal 3, pp 68-71, 2018.  https://doi.org/10.1016/j.fcij.2017.12.001
  14. Garima Singh, Dr M.A.Ansari, "Efficient Detection of Brain Tumor from MRIs Using K-Means Segmentation and Normalized Histogram", IEEE, 1st India International Conference on Information Processing (IICIP), 2016. 
  15. Parnian Afshar, Konstantinos N. Plantaniotis, Arash Mohammadi, "Capsule Networks for brain tumor classification based on MRI images coarse tumor boundaries", IEEE, International Conference on Acoustics, Speech and Signal Processing, 2019. 
  16. Hum, Yan Chai; Lai, Khin Wee; Mohamad Salim, Maheza Irna (October 11, 2014). "Multiobjectives bihistogram equalization for image contrast enhancement". Complexity. 20 (2): 22-36. Bibcode:2014Cmplx..20b..22H. doi:10.1002/cplx.21499. 
  17. Howard, Andrew G. and Zhu, Menglong and Chen, Bo and Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and Andreetto, Marco and Adam, Hartwig (2017), MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv.org perpetual, nonexclusive license, 10.48550/ARXIV.1704.04861. 
  18. Simonyan, Karen and Zisserman, Andrew (2014), Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv.org perpetual, non-exclusive license, 10.48550/ARXIV.1409.1556. 
  19. Szegedy, Christian and Ioffe, Sergey and Vanhoucke, Vincent and Alemi, Alex (2016), Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, arXiv.org perpetual, non-exclusive license, 10.48550/ARXIV.1602.07261. 
  20. Szegedy, Christian and Vanhoucke, Vincent and Ioffe, Sergey and Shlens, Jonathon and Wojna, Zbigniew (2015), Rethinking the Inception Architecture for Computer Vision, arXiv.org perpetual, non-exclusive license, 10.48550/ARXIV.1512.00567. 
  21. Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q. (2016), Densely Connected Convolutional Networks, arXiv.org perpetual, non-exclusive license, 10.48550/ARXIV.1608.06993. 
  22. https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection.