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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Analysis of Brain Tumor Using MRI Images


DOI: http://dx.doi.org/10.15439/2022F69

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 201204 ()

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Abstract. The increasing rates of deadly brain tumors inhumans correspondingly increase the need for highly experienced medical personnel for diagnosis and treatment. Therefore, to reduce the workload and the time from suspicion of disease to diagnosis, then plan for suitable treatment, there is a need to automate the initial part of the process by implementing a Computer-Aided-Disease-Diagnosis (CADD) system for brain tumor classification. By studying the types of tumors involved, how the convolutional neural network works, some of its pretrained models, and their application in brain tumor classification, the likelihood of producing a promising CADD system heavily increases. The research shows that the DenseNet121 architecture, either fully trained or using transfer learning, likely is the most appropriate candidate for the CADD system in development.


  1. D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, “The 2007 WHO classification of tumours of the central nervous system,” Acta neuropathologica, vol. 114, no. 2, pp. 97,109, Aug. 2007.
  2. L. S. Hu, A. Hawkins-Daarud, L. Wang, J. Li, and K. R. Swanson, “Imaging of intratumoral heterogeneity in high-grade glioma,” Cancer letters, vol. 477, pp. 97,106, May 2020. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/32112907
  3. D. A. Forst, B. V. Nahed, J. S. Loeffler, and T. T. Batchelor, “Low-grade gliomas,” The oncologist, vol. 19, no. 4, pp. 403,413, Apr. 2014. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/24664484
  4. Q. Luo, Y. Li, L. Luo, and W. Diao, “Comparisons of the accuracy of radiation diagnostic modalities in brain tumor: A nonrandomized, nonexperimental, cross-sectional trial,” Medicine, vol. 97, no. 31, Aug. 2018. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/30075495
  5. C.-X. Wu, G.-S. Lin, Z.-X. Lin, J.-D. Zhang, L. Chen, S.-Y. Liu, W.-L. Tang, X.-X. Qiu, and C.-F. Zhou., “Peritumoral edema on magnetic resonance imaging predicts a poor clinical outcome in malignant glioma,” Oncology Letters, vol. 10, no. 5, pp. 2769,2776, Aug. 2015. [Online]. Available: https://www.spandidos-publications.com/10.3892/ol.2015.3639
  6. K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, vol. 36, no. 4, pp. 193,202, Apr. 1980. [Online]. Available: https://link.springer.com/article/10.1007/BF00344251
  7. ——, “Cognitron: A self-organizing multilayered neural network,” Biological Cybernetics, vol. 20, no. 3, pp. 121–136, Sep. 1975. [Online]. Available: https://doi.org/10.1007/BF00342633
  8. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278,2324, Nov. 1998. [Online]. Available: http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
  9. J. Kang, Z. Ullah, and J. Gwak, “MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers,” Sensors, vol. 21, no. 6, Mar. 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/6/2222
  10. L. Scarpace, A. E. Flanders, R. Jain, T. Mikkelsen, and D. W. Andrews, “Data From REMBRANDT [Data set],” 2019. [Online]. Available: https://wiki.cancerimagingarchive.net/display/Public/REMBRANDT#35392299515cc672b974080a1394cbe9c649c74
  11. S. Khawaldeh, U. Pervaiz, A. Rafiq, and R. S. Alkhawaldeh, “Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks,” Applied Sciences, vol. 8, no. 1, 2018. [Online]. Available: https://www.mdpi.com/2076-3417/8/1/27
  12. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations, Sep. 2015. [Online]. Available: https://arxiv.org/pdf/1409.1556.pdf
  13. O. N. Belaid and M. Loudini, “Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN,” Journal of Information Technology Management, vol. 12, no. 2, pp. 13,25, 2020. [Online]. Available: https://jitm.ut.ac.ir/article 75788.html
  14. O. Sevli, “Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images / Beyin MR Görüntülerini Sınıflandırmada Farklı Önceden Eğitilmiş Derin Öğrenme Modellerinin Performans Karşılaştırması,” Acta Infologica, vol. 5, p. 2021, Jun. 2021. [Online]. Available: https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/99DD9C496BF14E44859851B33E49A006
  15. A. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” ArXiv, 04 2017. [Online]. Available: https://arxiv.org/pdf/1704.04861.pdf
  16. S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated Residual Transformations for Deep Neural Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Jul. 2017, pp. 5987,5995. [Online]. Available: https://ieeexplore.ieee.org/document/8100117
  17. G. Huang, Z. Liu, G. Pleiss, L. Van Der Maaten, and K. Weinberger, “Convolutional Networks with Dense Connectivity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1,1, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8721151