한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제25권8호
  • /
  • Pages.1233-1241
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  • 2022
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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A Deep Learning Method for Brain Tumor Classification Based on Image Gradient

  • Long, Hoang (Department of Artificial Intelligence Convergence, Pukyong National University) ;
  • Lee, Suk-Hwan (Dept. of Computer Engineering, Dong-A University) ;
  • Kwon, Seong-Geun (Department of Electronics Engineering, Kyungil University) ;
  • Kwon, Ki-Ryong (Department of Artificial Intelligence Convergence, Pukyong National University)
  • 투고 : 2022.08.02
  • 심사 : 2022.08.25
  • 발행 : 2022.08.31
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초록

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

키워드

과제정보

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2020R1I1A306659411, 2020R1F1A1069124) and the Ministry of Trade, Industry and Energy for its financial support of the project titled "the establishment of advanced marine industry open laboratory and development of realistic convergence content.

참고문헌

  1. Classification of Brain Tumors, https://www.aans.org/en/Media/Classifications-of-Brain-Tumors (Accessed on 8 June 2022).
  2. A. Rehman, S. Naz, M.I. Razzak, F. Akram, and M. Imran, "A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning," Circuits, Systems, and Signal Processing, Vol. 39, No. 2, pp. 757-775, 2019.
  3. A. Pashaei, H. Sajedi, and N. Jazayeri, "Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines," 8th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 314-319, 2018.
  4. A. Lakshmi and T. Arivoli, "Computer Aided Diagnosis System for Brain Tumor Detection And Segmentation," Journal of Theoretical & Applied Information Technology, Vol. 64, No. 2, pp. 561-597, 2014.
  5. R. Nalbalwar, U. Majhi, R. Patil, and S. Gonge, "Detection of Brain Tumor by Using ANN," International Journal of Research in Advent Technology, Vol. 2, No. 4, pp. 279-282, 2014.
  6. P. John, "Brain Tumor Classification Using Wavelet and Texture Based Neural Network," International Journal of Scientific & Engineering Research, Vol. 3, No. 10, pp. 1-7, 2012.
  7. Z.C. Mehneh and R. Shamsaee, "Content Base Image Retrieval Design & Optimization for MRI Brain Tumor Images," Proceeding of the 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), pp. 152-157, 2017.
  8. J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, et al., "Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition," PloS one, Vol. 10, No. 10, e0140381, 2015.
  9. Y. Pan, W. Huang, Z. Lin, W. Zhu, J. Zhou, J. Wong, and Z. Ding, "Brain Tumor Grading Based on Neural Networks and Convolutional Neural Networks," Proceeding of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 699-702, 2015.
  10. N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, and T.R. Mengko, "Brain Tumor Classification Using Convolutional Neural Network," World Congress on Medical Physics and Biomedical Engineering, pp 183-189, 2018.
  11. P. Afshar, A. Mohammadi, and K.N. Plataniotis, "Brain Tumor Type Classification via Capsule Networks," arXiv Preprint, arXiv:1802.10200, 2018.
  12. R. Tian, G. Sun, X. Liu, and B. Zheng, "Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising," Electronics, Vol. 10, No. 6, 655, 2021.
  13. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper with Convolutions," Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, 2015.
  14. G. Huang, Z. Liu, L. Maaten, and K.Q. Weinberger, "Densely Connected Convolutional Networks," Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017.
  15. I. Kandel and M. Castelli, "Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A ReView," Applied Sciences, Vol. 10, No. 6, 2021, 2020.
  16. J.-Y. Jeon, D.-Y. Park, S.-Y. Lim, Y.-S. Ji, and S.-B. Lim, "Automatic Extraction of Hangul Stroke Element Using Faster R-CNN for Font Similarity," Journal of Korea Multimedia Society, Vol. 23, No. 8, pp. 953-964, 2020. https://doi.org/10.9717/KMMS.2020.23.8.953
  17. J. Wang, X. Hua, and X. Zeng, "Spectral-Based SPD Matrix Representation for Signal Detection Using a Deep Neutral Network," Entropy, Vol. 22, No. 5, 585, 2020.
  18. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016
  19. J. Cheng, Brain Tumor Dataset, https://doi.org/10.6084/m9.figshare.1512427.v5 (Accessed on June 8, 2022).
  20. D. Jacobs, Image Gradients, https://www.cs.umd.edu/~djacobs/CMSC426/ImageGradients.pdf (Accessed on June 8, 2022).