BRAIN TUMOR CLASSIFICATION USING INCEPTIONRESNET-V2 AND TRANSFER LEARNING APPROACH
Abstract
Brain, a highly intricate organ within the central nervous system, plays a fundamental role in information processing, cognition, motor control, and consciousness. Brain tumors pose severe threats to brain function and overall human well-being. Timely detection of these tumors is imperative for life-saving interventions. A dataset comprising four categories: no tumors, meningioma tumors, glioma tumors, and pituitary tumors was regarded in this research. The employed of the InceptionResNet-V2 architecture combined with Transfer Learning and data augmentation proposed to obtain optimal results on brain tumor classification types. Transfer learning act as fine tuning, enabling the model to acquire fundamental low-level image features from a comprehensive dataset. It then leverages higher-level features to become more tailored to the specific training data. This method is employed to improve the model's adaptability to the training data. The InceptionResNet-V2 architecture utilized in the evaluation using test data, in Scenario 1, achieved 94.18% accuracy. Scenario 2, which combined augmentation with InceptionResNetV2, showed an improvement in accuracy to 95.10%. Furthermore, in Scenario 3, the combination of InceptionResNetV2 with Transfer Learning and augmentation resulted in an impressive accuracy of 96.63%, demonstrating its effectiveness in brain tumor classification. Transfer learning aligns the model by acquiring low-level image features and utilizing higher-level features to improve adaptability to the training data.
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K. N. Qodri, I. Soesanti, and H. A. Nugroho, Krisna Nuresa Qodri “Image analysis for MRI-based brain tumor classification using deep learning”, IJITEE (International Journal of Information Technology and Electrical Engineering), vol. 5, no.1, pp. 21-28, 2021, doi: 10.22146/ijitee.62663.
O. Özkaraca et al., “Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images,” Life, vol. 13, no. 2, Feb. 2023, doi: 10.3390/life13020349.
K. Pattabiraman, S. K. Muchnik, and N. Sestan, “The evolution of the human brain and disease susceptibility,” Curr Opin Genet Dev, vol. 65, pp. 91–97, 2020, doi: 10.1016/j.gde.2020.05.004.
F. J. Díaz-Pernas, M. Martínez-Zarzuela, D. González-Ortega, and M. Antón-Rodríguez, “A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network,” Healthcare (Switzerland), vol. 9, no. 2, Feb. 2021, doi: 10.3390/healthcare9020153.
J. Kang, Z. Ullah, and J. Gwak, “Mri-based brain tumor classification using ensemble of deep features and machine learning classifiers,” Sensors, vol. 21, no. 6, pp. 1–21, 2021, doi: 10.3390/s21062222.
Senan, E. M., Jadhav, M. E., Rassem, T. H., Aljaloud, A. S., & Mohammed, B. A. (2022). “Early diagnosis of brain tumour MRI images using hybrid techniques between deep and machine learning”. Computational and Mathematical Methods in Medicine, 2022, Article ID 8330833, 17 pages. https://doi.org/10.1155/2022/8330833
A. Raza et al., “A Hybrid Deep Learning-Based Approach for Brain Tumor Classification,” Electronics (Switzerland), vol. 11, no. 7, Apr. 2022, doi: 10.3390/electronics11071146.
H. Dave, N. Kant, N. Dave, and D. Ghorui, “Brain Tumor Classification Using Deep Learning,” pp. 155-175, 2021. [Online]. Available: http://www.ijeast.com
R. Andre, B. Wahyu, and R. Purbaningtyas, “Klasifikasi Tumor Otak Menggunakan Convolutional Neural Network Dengan Arsitektur Efficientnet-B3,” JUST IT: Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, vol. 12, no.3, 2021, doi: 10.24853/justit.12.3.55-59.
R. Rakhman Wahid, F. Tri Anggraeni, and B. Nugroho, “Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging,” Prosiding Seminar Nasional Informatika Bela Negara, vol. 1, pp. 16-20, 2020.
D. R. Nayak, N. Padhy, P. K. Mallick, M. Zymbler, and S. Kumar, “Brain Tumor Classification Using Dense Efficient-Net,” Axioms, vol. 11, no. 1, Jan. 2022, doi: 10.3390/axioms11010034.
W. Hastomo and S. dan Sudjiran, “Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak,” Seminar Nasional Teknologi Informasi dan Komunikasi STI&K (SeNTIK), vol. 5, no. 1, 2021, [online]. Available: https://ejournal.jak-stik.ac.id/index.php/sentik/article/view/3355.
Magadza, T.; Viriri, S. “Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art” Journal of Imaging, vol. 7, no. 2, 2021, doi: https://doi.org/10.3390/ jimaging7020019.
Khan, F., Ayoub, S., Gulzar, Y., Majid, M., Reegu, F. A., Mir, M. S., Soomro, A. B., & Elwasila, O. (2023). MRI-based effective ensemble frameworks for predicting human brain tumor”, Journal of Imaging, vol. 9, no. 8, p. 163, 2023, https://doi.org/10.3390/jimaging9080163
H. A. Khan, W. Jue, M. Mushtaq, and M. U. Mushtaq, “Brain tumor classification in MRI image using convolutional neural network,” Mathematical Biosciences and Engineering, vol. 17, no. 5, pp. 6203–6216, 2020, doi: 10.3934/MBE.2020328.
A. E. Minarno, M. Hazmi Cokro Mandiri, Y. Munarko, and H. Hariyady, “Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, vol. 4, 2021, doi: 10.22219/kinetik.v6i2.1219.
C. Srinivas et al., “Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images,” J Healthc Eng, vol. 2022, 2022, doi: 10.1155/2022/3264367.
M. M. Badža and M. C. Barjaktarović, “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.
M. N. Winnarto, M. Mailasari, and A. Purnamawati, “Klasifikasi Jenis Tumor Otak Menggunakan Arsitekture Mobilenet V2,” Jurnal SIMETRIS, vol. 13, no. 2, 2022, doi: 10.24176/simet.v13i2.8821.
S. Saeedi, S. Rezayi, H. Keshavarz, and S. R. Niakan Kalhori, “MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques,” BMC Med Inform Decis Mak, vol. 23, no. 1, Dec. 2023, doi: 10.1186/s12911-023-02114-6.
A. Thomas, P. M. Harikrishnan, P. Palanisamy, and V. P. Gopi, “Moving Vehicle Candidate Recognition and Classification Using Inception-ResNet-v2,” in Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020, Institute of Electrical and Electronics Engineers Inc., Jul. 2020, pp. 467–472. doi: 10.1109/COMPSAC48688.2020.0-207.
Plested, J., & Gedeon, T. (2022). “Deep transfer learning for image classification: a survey”. a survey. arXiv preprint arXiv:2205.09904.], 2022, [online]. Available: https://doi.org/10.1234/example.
Hilal, A. M., Al-Wesabi, F. N., Alzahrani, K. J., Al Duhayyim, M., Hamza, M. A., Rizwanullah, M., & García Díaz, V. (2022). Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model. European Journal of Remote Sensing, 55(sup1), 12-23. https://doi.org/10.1080/22797254.2021.2017799
Didih Rizki Chandranegara, Jafar Shodiq Djawas, Faiq Azmi Nurfaizi, and Zamah Sari, “Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method,” Jurnal Online Informatika, vol. 8, no. 1, pp. 61–71, Jun. 2023, doi: 10.15575/join.v8i1.1051.
S. Bhuvaji, A. Kadam, P. Bhumkar, and S. Dedge, “Brain Tumor Classification (MRI),” Kaggle, 2020.
Tandel, G. S., Balestrieri, A., Jujaray, T., Khanna, N. N., Saba, L., & Suri, J. S. “Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm” Computers in Biology and Medicine, 122, p. 103804, 2020, doi: 10.1016/j.compbiomed.2020.103804
Habib, G., & Qureshi, S.. Optimization and acceleration of convolutional neural networks: A survey. Journal of King Saud University - Computer and Information Sciences, 34, 2022, 4244-4268. doi:10.1016/j.jksuci.2020.10.004.
T. S. Azzahra, J. J. Cerelia, F. Azhar, L. Nugraha, and A. A. Pravitasari. “Enthusiastic International Journal Of Applied Statistics And Data Science MRI-Based Brain Tumor Classification Using Inception Resnet V2”, 2023, [Online].Available:https://journal.uii.ac.id/ENTHUSIASTIC
J. Wang, X. He, S. Faming, G. Lu, H. Cong, and Q. Jiang, “A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure,” IEEE Access, vol. 9, pp. 93209–93223, 2021, doi: 10.1109/ACCESS.2021.3093210.
P. Chhikara, P. Singh, P. Gupta, and T. Bhatia, “Deep convolutional neural network with transfer learning for detecting pneumonia on chest x-rays,” in Advances in Intelligent Systems and Computing, Springer, 2020, pp. 155–168. doi: 10.1007/978-981-15-0339-9_13.
Abou Baker, N., Zengeler, N., & Handmann, U. (2022). A Transfer Learning Evaluation of Deep Neural Networks for Image Classification. Machine Learning and Knowledge Extraction, vol. 4, no. 1, 22-41. doi:10.3390/make4010002.
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. "A comprehensive survey on transfer learning." Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, 2020, doi: 10.1109/JPROC.2020.3004555
Kandel, I., & Castelli, M. (2020). "Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review." Applied Sciences vol. 10, no. 6, 2021, doi: 10.3390/app10062021.
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