Effect of Optimisation in Brain Tumour Classification with CNN-VIT Hybrid
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Keywords:
Hibrida, Tumor Otak, Kecerdasan Buatan
Abstract
A tumor is an abnormality in cells that causes cells that should live and die at a certain time to remain alive and grow abnormally. Tumors can affect all parts of the human body such as the brain. In diagnosing tumors, artificial intelligence can be used to perform classification or detection quickly. The use of artificial intelligence such as Convolutional Neural Network (CNN) has been commonly used to enter the medical field. Besides CNN, there are also other algorithms such as Vision Transformer (ViT) to do similar work. Therefore, a hybrid of the two methods is used to support the advantages of each algorithm and get satisfactory results. The results of the CNN-ViT hybrid used in this study obtained the highest results with an Adam Optimization with Learning Rate of 0.001% with an accuracy of 94%, recall and f1 score of 94% and precision of 95%.
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References
[1] M. Kristian, S. Andryana, and A. Gunayarti, “Diagnosa Penyakit Tumor Otak Menggunakan Metode Waterfall dan Algoritma Depth First Search,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 6, pp. 11–24, Jun. 2021.
[2] K. N. Deeksha, M. Deeksha, A. V Girish, A. S. Bhat, and H. Lakshmi, “Classification of Brain Tumor and its types using Convolutional Neural Network,” in 2020 IEEE International Conference for Innovation in Technology (INOCON), IEEE, 2020, pp. 1–6.
[3] M. N. Winnarto, M. Mailasari, and A. Purnamawati, “Klasifikasi Jenis Tumor Otak Menggunakan Arsitektur Mobile Net V2,” Jurnal SIMETRIS, vol. 13, no. 2, 2022.
[4] M. Hanindia, P. Swari, G. Ngurah, and A. Mahendra, “SISTEM PAKAR SKRINING PENYAKIT YANG DISEBABKAN OLEH VIRUS MENGGUNAKAN CERTAINTY FACTOR,” Jurnal Mantik Penusa, vol. 3, no. 1, pp. 196–203, 2019.
[5] D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. doi: 10.3390/electronics10202470.
[6] K. Mochammad et al., “IMPLEMENTASI ARSITEKTUR ALEXNET DAN RESNET34 PADA KLASIFIKASI CITRA PENYAKIT DAUN KENTANG MENGGUNKAN TRANSFER LEARNING,” 2023.
[7] T. T. Nguyen, T. V. Nguyen, and M. T. Tran, “Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic,” IEEE Access, vol. 11, pp. 95346–95357, 2023, doi: 10.1109/ACCESS.2023.3307014.
[8] A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems 30, Dec. 2017.
[9] W. Liu et al., “PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Mar. 2022, pp. 235–244. [Online]. Available: http://arxiv.org/abs/2203.04568
[10] R. Rakhman Wahid, F. Tri Anggraeny, and B. Nugroho, “Brain Tumor Classification with Hybrid Algorithm Convolutional Neural Network-Extreme Learning Machine,” 2021.
[11] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, and M. O. Alassafi, “Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method,” Computers, Materials and Continua, vol. 67, no. 3, pp. 3967–3982, Mar. 2021, doi: 10.32604/cmc.2021.014158.
[12] A. Peryanto, A. Yudhana, and D. R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” 2019. [Online]. Available: https://www.mathworks.com/discovery/convolutional-neural-network.html
[13] J. C. Ye, “Convolutional Neural Networks,” Geometry of Deep Learning: A Signal Processing Perspective, pp. 113–134, 2022.
[14] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” International Conference on Learning Representations, Oct. 2020, [Online]. Available: http://arxiv.org/abs/2010.11929
[15] J. A. Figo, N. Yudistira, and A. W. Widodo, “Deteksi Covid-19 dari Citra X-ray menggunakan Vision Transformer,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 3, pp. 1116–1125, Mar. 2023, [Online]. Available: http://j-ptiik.ub.ac.id
[16] O. N. Putri, “Implementasi Metode Cnn Dalam Klasifikasi Gambar Jamur Pada Analisis Image Processing (Studi Kasus: Gambar Jamur Dengan Genus Agaricus Dan Amanita),” Undegraduate Thesis, Universitas Islam Indonesia, Yogyakarta, 2020.
[17] D. Soydaner, “A Comparison of Optimization Algorithms for Deep Learning,” Intern J Pattern Recognit Artif Intell, vol. 34, no. 13, Dec. 2020, doi: 10.1142/S0218001420520138.
[18] A. Wibowo, P. W. Wiryawan, and N. I. Nuqoyati, “Optimization of neural network for cancer microRNA biomarkers classification,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Jun. 2019. doi: 10.1088/1742-6596/1217/1/012124.
[19] S. Ahlawat and A. Choudhary, “Hybrid CNN-SVM Classifier for Handwritten Digit Recognition,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2554–2560. doi: 10.1016/j.procs.2020.03.309.
[20] D. Lovell, D. Miller, J. Capra, and A. Bradley, “Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2206.02157
[21] H. A. P. Belangi, “Komparasi Performa Algoritma Convolutional Neural Network,” Undergraduate Thesis, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, 2023.
[2] K. N. Deeksha, M. Deeksha, A. V Girish, A. S. Bhat, and H. Lakshmi, “Classification of Brain Tumor and its types using Convolutional Neural Network,” in 2020 IEEE International Conference for Innovation in Technology (INOCON), IEEE, 2020, pp. 1–6.
[3] M. N. Winnarto, M. Mailasari, and A. Purnamawati, “Klasifikasi Jenis Tumor Otak Menggunakan Arsitektur Mobile Net V2,” Jurnal SIMETRIS, vol. 13, no. 2, 2022.
[4] M. Hanindia, P. Swari, G. Ngurah, and A. Mahendra, “SISTEM PAKAR SKRINING PENYAKIT YANG DISEBABKAN OLEH VIRUS MENGGUNAKAN CERTAINTY FACTOR,” Jurnal Mantik Penusa, vol. 3, no. 1, pp. 196–203, 2019.
[5] D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics (Switzerland), vol. 10, no. 20. MDPI, Oct. 01, 2021. doi: 10.3390/electronics10202470.
[6] K. Mochammad et al., “IMPLEMENTASI ARSITEKTUR ALEXNET DAN RESNET34 PADA KLASIFIKASI CITRA PENYAKIT DAUN KENTANG MENGGUNKAN TRANSFER LEARNING,” 2023.
[7] T. T. Nguyen, T. V. Nguyen, and M. T. Tran, “Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic,” IEEE Access, vol. 11, pp. 95346–95357, 2023, doi: 10.1109/ACCESS.2023.3307014.
[8] A. Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems 30, Dec. 2017.
[9] W. Liu et al., “PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Mar. 2022, pp. 235–244. [Online]. Available: http://arxiv.org/abs/2203.04568
[10] R. Rakhman Wahid, F. Tri Anggraeny, and B. Nugroho, “Brain Tumor Classification with Hybrid Algorithm Convolutional Neural Network-Extreme Learning Machine,” 2021.
[11] N. Noreen, S. Palaniappan, A. Qayyum, I. Ahmad, and M. O. Alassafi, “Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method,” Computers, Materials and Continua, vol. 67, no. 3, pp. 3967–3982, Mar. 2021, doi: 10.32604/cmc.2021.014158.
[12] A. Peryanto, A. Yudhana, and D. R. Umar, “Rancang Bangun Klasifikasi Citra Dengan Teknologi Deep Learning Berbasis Metode Convolutional Neural Network,” 2019. [Online]. Available: https://www.mathworks.com/discovery/convolutional-neural-network.html
[13] J. C. Ye, “Convolutional Neural Networks,” Geometry of Deep Learning: A Signal Processing Perspective, pp. 113–134, 2022.
[14] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” International Conference on Learning Representations, Oct. 2020, [Online]. Available: http://arxiv.org/abs/2010.11929
[15] J. A. Figo, N. Yudistira, and A. W. Widodo, “Deteksi Covid-19 dari Citra X-ray menggunakan Vision Transformer,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 3, pp. 1116–1125, Mar. 2023, [Online]. Available: http://j-ptiik.ub.ac.id
[16] O. N. Putri, “Implementasi Metode Cnn Dalam Klasifikasi Gambar Jamur Pada Analisis Image Processing (Studi Kasus: Gambar Jamur Dengan Genus Agaricus Dan Amanita),” Undegraduate Thesis, Universitas Islam Indonesia, Yogyakarta, 2020.
[17] D. Soydaner, “A Comparison of Optimization Algorithms for Deep Learning,” Intern J Pattern Recognit Artif Intell, vol. 34, no. 13, Dec. 2020, doi: 10.1142/S0218001420520138.
[18] A. Wibowo, P. W. Wiryawan, and N. I. Nuqoyati, “Optimization of neural network for cancer microRNA biomarkers classification,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Jun. 2019. doi: 10.1088/1742-6596/1217/1/012124.
[19] S. Ahlawat and A. Choudhary, “Hybrid CNN-SVM Classifier for Handwritten Digit Recognition,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2554–2560. doi: 10.1016/j.procs.2020.03.309.
[20] D. Lovell, D. Miller, J. Capra, and A. Bradley, “Never mind the metrics -- what about the uncertainty? Visualising confusion matrix metric distributions,” Jun. 2022, [Online]. Available: http://arxiv.org/abs/2206.02157
[21] H. A. P. Belangi, “Komparasi Performa Algoritma Convolutional Neural Network,” Undergraduate Thesis, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, 2023.
Published
2024-06-10
How to Cite
[1]
I. C. Sukandar, F. Tri Anggraeny, and M. Hanindia Prami Swari, “Effect of Optimisation in Brain Tumour Classification with CNN-VIT Hybrid”, antivirus, vol. 18, no. 1, pp. 112-124, Jun. 2024.
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Articles
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