Klasifikasi Citra MRI Tumor Otak Menggunakan Metode Convolutional Neural Network
DOI:
https://doi.org/10.32877/bt.v7i1.1576
Keywords:
Augmentasi Data, Convoutional Neural Network, Klasifikasi Citra , Magnetic Resonance Imaging, Tumor Otak
Abstract
This study aims to improve the accuracy of brain tumor classification using Convolutional Neural Network (CNN) method on MRI images. In this study, various experiments were conducted using the original dataset and data that had undergone augmentation to increase the amount and variety of data. This study shows that data augmentation, such as flipping, scaling, and rotation, significantly improves model accuracy. The best model was obtained using flip and scale augmentation techniques with an average accuracy of 92.97%. These results show that the use of data augmentation techniques can improve the performance of CNN models in classifying brain tumors, and reduce the risk of overfitting. This research makes an important contribution to the field of medical diagnosis by providing a more accurate and efficient model for detecting brain tumors.
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References
H. Pengobatan Klinis, M. Ghozali, H. Sumarti, K. Kunci, T. Otak, and O. Dewasa, “Pengobatan Klinis Tumor Otak pada Orang Dewasa,” Jurnal Pendidikan Fisika dan Fisika Terapan, vol. 6, no. 1, p. 2020, 2020.
“Apa itu Tumor Otak? Ini Penyebab, Gejala, dan Pengobatannya .” Accessed: Jul. 31, 2024. [Online]. Available: https://www.siloamhospitals.com/informasi-siloam/artikel/apa-itu-tumor-otak#mcetoc_1gs9brk413if
“Brain MRI.” Accessed: Jul. 31, 2024. [Online]. Available: https://my.clevelandclinic.org/health/diagnostics/22966-brain-mri
M. N. Winnarto, M. Mailasari, and A. Purnamawati, “KLASIFIKASI JENIS TUMOR OTAK MENGGUNAKAN ARSITEKTURE MOBILENET V2,” Jurnal SIMETRIS, vol. 13, no. 2, 2022.
A. S. Febrianti, T. A. Sardjono, and A. F. Babgei, “Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine,” JURNAL TEKNIK ITS, vol. 9, no. 1, pp. 118–123, 2020.
F. Nurona Cahya et al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network ( CNN).” [Online]. Available: http://sistemasi.ftik.unisi.ac.id
A. Nabilla Zulfa, M. Irsyad, F. Yanto, and S. Sanjaya, “JURNAL MEDIA INFORMATIKA BUDIDARMA Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi,” 2023, doi: 10.30865/mib.v7i2.6056.
M. M. Badža and M. C. Barjaktarovic, “Classification of brain tumors from mri images using a convolutional neural network,” Applied Sciences (Switzerland), vol. 10, no. 6, Mar. 2020, doi: 10.3390/app10061999.
D. Gunawan and H. Setiawan, “Convolutional Neural Network dalam Analisis Citra Medis,” 2022.
W. Budiharto and B. S. Abbas, Panduan Riset & Publikasi Penelitian bagi Akademisi, 1st ed. Yogyakarta: Andi Publisher, 2023.
D. Bhatt et al., “Cnn variants for computer vision: History, architecture, application, challenges and future scope,” Oct. 01, 2021, MDPI. doi:10.3390/electronics10202470.
P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image data augmentation techniques for deep learning applications,” Aug. 01, 2021, John Wiley and Sons Inc. doi: 10.1111/1754-9485.13261.
S. Yang, W. Xiao, M. Zhang, S. Guo, J. Zhao, and F. Shen, “Image Data Augmentation for Deep Learning: A Survey,” Apr. 2022, [Online]. Available: http://arxiv.org/abs/2204.08610
Q. Wen et al., “Time Series Data Augmentation for Deep Learning: A Survey,” in IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 4653–4660. doi: 10.24963/ijcai.2021/631.
M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.
K. P. Shung, “Accuracy, Precision, Recall or F1?” Accessed: Jul. 13, 2024. [Online]. Available: https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9
N. Das and S. Das, “Epoch and accuracy based empirical study for cardiac MRI segmentation using deep learning technique,” PeerJ, vol. 11, p. e14939, Mar. 2023, doi: 10.7717/peerj.14939.
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