Convolutional Neural Network (CNN) Prediction on Meningioma, Glioma with Tensorflow

Öz

Brain tumors can significantly affect a patient's life in a variety of ways. Classification of brain tumors is also important. Artificial intelligence (AI) techniques such as machine learning and deep learning can be very beneficial to physicians to classify tumors based on various parameters. In this study, the dataset is comprised of two distinct components which were prepared specifically for testing and training purposes, respectively. TensorFlow software library was used to utilize of Convolutional Neural Network (CNN). Since the most suitable weight values to solve the problem in deep learning are calculated step by step, the performance in the first epochs was low and unstable compared to the progressive values, and the performance increased as the number of epochs increased. However, after a certain step, the learning status of our model decreased considerably. The accuracy of the created model was observed to reach 0,90. As a result, as stated in its intended use, a mechanism that helps physicians and uses time efficiently has been successfully developed. In order to obtain more efficient results, the data set used in the study can be expanded, allowing deep learning models to work more effectively.

Anahtar Kelimeler

• classification
• brain tumor
• Artificial Intelligence
• Machine Learning
• Tensorflow

Kaynakça

1. [1] Lin, N. U., Lee, E. Q., & Aoyama, H. (2015). Challenges relating to solid tumour brain metastases in clinical trials, part 1: patient population, response, and progression. A report from the RANO group. The Lancet Oncology, 16(10);e419-e426. doi: 10.1016/s1470-2045(15)00096-2
2. [2] https://www.abta.org/brain-tumor-facts-statistics/
3. [3] Louis, David N., et al. (2021). The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology, 23(8);1231-1251, https://doi.org/10.1093/neuonc/noab064.
4. [4] Ghafoorian, Mohsen, et al. (2018). Deep Learning-Based Classification of Diffuse Gliomas Using MR Imaging. Radiology, 281(3);907-918, https://doi.org/10.1148/radiol.2018181748.
5. [5] Mandonnet, E., Duffau, H., Bauchet, L., & Almairac, F. (2010). Misdiagnosis of brain tumors: incidence and guidelines for avoidance. Journal of Neurosurgery, 112(2);467-473.
6. [6] Shin, J. Y., Kim, E. H., Cho, B. K., & Kim, S. H. (2015). Inappropriate treatment decisions for gliomas due to misclassification of tumor grade. Journal of Neuro-Oncology, 121(1); 85-92.
7. [7] Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., ... & Ellison, D. W. (2014). The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica, 131(6);803-820.
8. [8] Weller, M., van den Bent, M., Tonn, J. C., Stupp, R., Preusser, M., Cohen-Jonathan-Moyal, E., ... & Reifenberger, G. (2015). European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Journal of Clinical Oncology, 33(25);2930-2936.

9. [9]Cancer Treatment Centers of America (n.d.). Types of Brain Cancer. https://www.cancercenter.com/cancer-types/brain-cancer/types, retrieved: 29.09.2020.
10. [10]Lopes MBS, Randenberg SR. Central nervous system. In Fletcher CDM, ed. Diagnostic Histopathology of Tumors. London: Livingstone, 2000:1607.
11. [11]Sartaj Bhuvaji, Ankita Kadam, Prajakta Bhumkar, Sameer Dedge, & Swati Kanchan. (2020). Brain Tumor Classification (MRI) [Data et]. Kaggle https://doi.org/10.34740/KAGGLE/DSV/1183165
12. [12]Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
13. [13]Weston, J., Ratle, F., & Collobert, R. (2008, July). Deep learning via semi-supervised embedding. In Proceedings of the 25th international conference on Machine learning (pp. 1168-1175).
14. [14]Shin, S. H., Bae, Y. E., Moon, H. K., Kim, J., Choi, S. H., Kim, Y., ... & Nah, J. (2017). Formation of triboelectric series via atomic-level surface functionalization for triboelectric energy harvesting. ACS nano, 11(6); 6131-6138.
15. [15]Bo Pang, Erik Nijkamp, Ying Nian Wu, (2020). Deep Learning With TensorFlow: A Review, UCLA
16. [16]Kiran Seetala, William Birdsong, Yenumula B. Reddy, Image Classification Using TensorFlow, 16th International Conference on Information Technology-New Generations (ITNG 2019), 2019, Volume 800, ISBN : 978-3-030-14069-4.