Image Analysis for MRI-Based Brain Tumor Classification using VGG16

.png)

Acknowledgements

• Saya telah mengambil dataset gambar termasuk gambar dari keaggle : https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri?select=Testing

• Jumlah dataset Training : 1217

• Jumlah dataset Validasi : 220

• Jumlah train kelas pengidap tumor : 822

• Jumlah train kelas tidak mengidap tumor : 395

• Jumlah validasi kelas pengidap tumor : 115

• Jumlah validasi kelas tidak mengidap tumor : 105

Image Analysis for MRI-Based Brain Tumor Classification

Using Deep Learning

Paper Utama

Krisna Nuresa Qodri1, Indah Soesanti2, Hanung Adi Nugroho3 IJITEE, Vol. 5, No. 1, March 2021, Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning https://www.mendeley.com/catalogue/ca2fe946-e9e7-37ec-80f9-1264e2a228b8/

Referensi

• C.F. Hotama P., H.A. Nugroho, and I. Soesanti, “Analisis Citra Otak pada Color-Task dan Word-Task dalam Stroop Task menggunakan Elektroencephanology (EEG),” Thesis, Universitas Gadjah Mada, Yogyakarta, Indonesia, 2014. http://etd.repository.ugm.ac.id/penelitian/detail/77397
• A.S. Febrianti, T.A. Sardjono, and A.F. Babgei, “Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine,” J. Tek. ITS, Vol. 9, No. 1, pp. A118-A123, 2020 https://ejurnal.its.ac.id/index.php/teknik/article/view/51587
• P. Afshar, K.N. Plataniotis, and A. Mohammadi, “Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries,” ICASSP 2019 - 2019 IEEE Int. Conf. on Acoustics, Speech and Signal Proc. (ICASSP), 2019, pp. 1368–1372 https://arxiv.org/abs/1811.00597
• N. Kumari and L. Gray, “Review of Brain Tumor Segmentation and Classification,” 2018 Int. Conf. Curr. Trends Towar. Converging Technol., 2018, pp. 1–6. https://ieeexplore.ieee.org/document/8551004/
• M. Mahmud, M.S. Kaiser, A. Hussain, and S. Vassanelli, “Applications of Deep Learning and Reinforcement Learning to Biological Data,” IEEE Trans. Neural Networks Learn. Syst., Vol. 29, No. 6, pp. 2063–2079, 2018. https://arxiv.org/abs/1711.03985
• M. Gurbină, M. Lascu, and D. Lascu, “Tumor Detection and Classification of MRI Brain Image Using Different Wavelet Transforms and Support Vector Machines,” 42nd International Conference on Telecommunications and Signal Processing (TSP), 2019, pp. 505–508 https://ieeexplore.ieee.org/document/8769040
• R. Vinoth and C. Venkatesh, “Segmentation and Detection of Tumor in MRI images Using CNN and SVM Classification,” 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), 2018, pp. 21–25 https://ieeexplore.ieee.org/document/8544306
• R. Ezhilarasi and P. Varalakshmi, “Tumor Detection in the Brain Using Faster R-CNN,” 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), 2018, pp. 388–392 https://ieeexplore.ieee.org/document/8714263
• H.E.M. Abdalla and M.Y. Esmail, “Brain Tumor Detection by Using Artificial Neural Network,” 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 2018, pp. 1–6. http://repository.sustech.edu/handle/123456789/11813
• M. Siar and M. Teshnehlab, “Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm,” 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 363–368. https://www.researchgate.net/publication/338797226_Brain_Tumor_Detection_Using_Deep_Neural_Network_and_Machine_Learning_Algorithm
• O. Bernard, A. Lalande, C. Zotti, et al., “Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?” IEEE Trans. Med. Imaging, Vol. 37, No. 11, pp. 2514–2525, 2018.https://hal.archives-ouvertes.fr/hal-01803621
• C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17), 2017, pp. 4278–4284.https://arxiv.org/abs/1602.07261
• K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations, 2015, pp. 1–14 https://dblp.org/rec/journals/corr/SimonyanZ14a.html
• G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, “Densely Connected Convolutional Networks,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269 https://arxiv.org/abs/1608.06993

Teknik Deep Learning yang digunakan

• Model dengan menggunakan algoritma VGG16

Authors

Kontributors dalam project ini adalah :

• Fikri Fahresi (201910370311406)
• Ronaldo Risky Samudra (201910370311408)