Brain Tumor Classification Using Four Versions of EfficientNet

Widi Hastomo, Adhitio Satyo Bayangkari Karno, Dody Arif, Indra Sari Kusuma Wardhana, Nada Kamilia, Rudy Yulianto, Aji Digdoyo, Tri Surawan

Abstract


Medical image processing approaches for detecting brain cancers are still primarily done manually, with low accuracy and taking a long period. Furthermore, this task can only be done by professionals with a high degree of medical competence, and the number of experts is obviously restricted in comparison to the large number of patients who need to be treated. With the growth of artificial intelligence and the rapid development of computers in terms of processing speed and storage capacity, it is feasible to assist doctors in classifying the existence of tumors in the head. This study employs four variations of the EfficientNet architecture to train a model on a variety of MRI imaging data. The model version B1 was shown to be the best in this investigation, with 98% accuracy, 99% precision, 95% recall, and 97% f1 score from versions B0 to B3 (4 versions). These results are excellent, but they do not rule out additional study utilizing various forms of design.

Keywords


Brain Tumor; EfficientNet; Deep Learning

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DOI: https://doi.org/10.15548/isrj.v3i01.5810
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References


W. K. Kleihues, P., & Cavenee, “Tumours of the nervous system.,” Lyon Int. Agency Res. Cancer, 2019.

M. Işın, A., Direkoğlu, C., & Şah, “Review of MRI-based brain tumor image segmentation using deep learning methods,” Procedia Comput. Sci., no. 102, pp. 317–324, 2016.

Radiological Society of North America, “RSNA-MICCAI Brain Tumor Radiogenomic Classification,” kaggle.com, 2022. https://www.kaggle.com/competitions/rsna-miccai-brain-tumor-radiogenomic-classification/data

A. Pulvirenti et al., “Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade,” JCO Clin. Cancer Informatics, no. 5, pp. 679–694, Jun. 2021, doi: 10.1200/CCI.20.00121.

T. Hossain, F. S. Shishir, M. Ashraf, M. D. A. Al Nasim, and F. Muhammad Shah, “Brain Tumor Detection Using Convolutional Neural Network,” in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019, pp. 1–6. doi: 10.1109/ICASERT.2019.8934561.

A. Yulianti, D., Syahruddin, E., Hudoyo, A., & Icksan, “Gejala Klinis Neurologis dan Gambaran CT Scan Otak Pasien Kanker Paru Karsinoma Bukan Sel Kecil Metastasis ke Otak di Rumah Sakit Persahabatan.,” Indones. J. Cancer, vol. 4, no. 1, 2010.

M. L. Bondy et al., “Brain tumor epidemiology: Consensus from the Brain Tumor Epidemiology Consortium,” Cancer, vol. 113, no. S7, pp. 1953–1968, Oct. 2008, doi: https://doi.org/10.1002/cncr.23741.

R. Riley, J. Murphy, and T. Higgins, “MRI imaging in pediatric appendicitis,” J. Pediatr. Surg. Case Reports, vol. 31, pp. 88–89, 2018, doi: https://doi.org/10.1016/j.epsc.2018.02.008.

R. C. Gonzalez, “Digital image processing.,” Pearson Educ. india, 2009.

W. Hastomo, “Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak,” vol. 5, no. Gambar 1, 2021.

W. Hastomo, A. Satyo, B. Karno, and N. Kalbuana, “Characteristic Parameters of Epoch Deep Learning to Predict Covid-19 Data in Indonesia,” 2021, doi: 10.1088/1742-6596/1933/1/012050.

S. Tripathy, R. Singh, and M. Ray, “Automation of Brain Tumor Identification using EfficientNet on Magnetic Resonance Images,” Procedia Comput. Sci., vol. 218, pp. 1551–1560, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.133.

B. Koonce, “EfficientNet BT - Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization,” B. Koonce, Ed. Berkeley, CA: Apress, 2021, pp. 109–123. doi: 10.1007/978-1-4842-6168-2_10.

C. Lemaréchal, “Cauchy and the gradient method,” Doc Math Extra, no. 10, pp. 251–154, 2012.

A. Satyo, B. Karno, W. Hastomo, I. Sari, K. Wardhana, and D. Arif, “29 Jenis Penyakit Tanaman Menggunakan Deep Learning EfficientNetB3 Identifikasi,” vol. 2, 2022.

R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu, “Neural Architecture Optimization,” in Advances in Neural Information Processing Systems, 2018, vol. 31. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/933670f1ac8ba969f32989c312faba75-Paper.pdf

J. He, K., Zhang, X., Ren, S., & Sun, “Deep residual learning for image recognition,” Proc. IEEE Conf. Comput. Vis. pattern Recognit., pp. 770–778, 2016.

Q. V. Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, “Mnasnet: Platform-aware neural architecture search for mobile,” Proc. IEEE/CVF Conf. Comput. Vis. pattern Recognit., pp. 2820–2828, 2019.

L. C. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” Proc. IEEE Conf. Comput. Vis. pattern Recognit., pp. 4510–4520, 2018.

N. Z. Ahmed, H. A., Hameed, A., & Bawany, “Network intrusion detection using oversampling technique and machine learning algorithms,” PeerJ Comput. Sci., vol. 8, 2022.

S. A. Alsaif, M. Sassi Hidri, H. A. Eleraky, I. Ferjani, and R. Amami, “Learning-Based Matched Representation System for Job Recommendation,” Computers, vol. 11, no. 11, 2022, doi: 10.3390/computers11110161.

P. Lin, K. Ye, and C.-Z. Xu, “Dynamic Network Anomaly Detection System by Using Deep Learning Techniques,” in Cloud Computing -- CLOUD 2019, 2019, pp. 161–176.

A. Yazdinejad, H. HaddadPajouh, A. Dehghantanha, R. M. Parizi, G. Srivastava, and M.-Y. Chen, “Cryptocurrency malware hunting: A deep Recurrent Neural Network approach,” Appl. Soft Comput., vol. 96, p. 106630, 2020, doi: https://doi.org/10.1016/j.asoc.2020.106630.

K. Kolesnikova, O. Mezentseva, and T. Mukatayev, “Analysis of Bitcoin Transactions to Detect Illegal Transactions Using Convolutional Neural Networks,” in 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), 2021, pp. 1–6. doi: 10.1109/SIST50301.2021.9465983.


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Gedung Fakultas Sains dan Teknologi 
Kampus III Universitas Islam Negeri Imam Bonjol Padang
Sungai Bangek, Kec. Koto Tangah, Kota Padang, Sumatera Barat

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