Comparison Analysis of Brain Image Classification Based on Thresholding Segmentation With Convolutional Neural Network
DOI:
https://doi.org/10.37385/jaets.v4i2.1583Keywords:
Convolutional Neural Network, Machine Learning, Magnetic Resonance Imaging, Classification, Brain TumorsAbstract
Brain tumor is one of the most fatal diseases that can afflict anyone regardless of gender or age necessitating prompt and accurate treatment as well as early discovery of symptoms. Brain tumors can be identified using Magnetic Resonance Imaging (MRI) to detect abnormal tissue or cell development in the brain and surrounding the brain. Biopsy is another option, but it takes approximately 10 to 15 days after the inspection, so technology is required to classify the image. The goal of this study is to conduct a comparative analysis of the greatest accuracy value attained while classifying using segmentation with thresholding versus segmentation without thresholding on the CNN method. Images are assigned threshold values of 150, 100, and 50. The dataset consists of 7023 MRI scans of four types of brain tumors: glioma, notumor, meningioma, and pituitary. Without utilising thresholding segmentation, the classification yielded the highest degree of accuracy, 92%. At the threshold of 100, classification by segmentation received the highest score of 88%. This demonstrates that thresholding segmentation during CNN model preprocessing is less effective for brain image classification
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Al-Hadidi, M. R., AlSaaidah, B., & Al-Gawagzeh, M. Y. (2020). Glioblastomas brain tumour segmentation based on convolutional neural networks. International Journal of Electrical and Computer Engineering, 10(5), 4738–4744. https://doi.org/10.11591/ijece.v10i5.pp4738-4744
Angeli, S., Emblem, K. E., Due-Tonnessen, P., & Stylianopoulos, T. (2018). Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI. NeuroImage: Clinical, 20(August), 664–673. https://doi.org/10.1016/j.nicl.2018.08.032
Arabahmadi, M., & Farahbakhsh, R. (2022). Deep Learning for Smart Healthcare: A Survey on Brain Tumor. Sensors, 22, 1–27. https://doi.org/https://doi.org/10.3390/ s22051960
Badr, C. E., Silver, D. J., Siebzehnrubl, F. A., & Deleyrolle, L. P. (2020). Metabolic heterogeneity and adaptability in brain tumors. Cellular and Molecular Life Sciences, 77(24), 5101–5119. https://doi.org/10.1007/s00018-020-03569-w
Baranwal, S. K., Jaiswal, K., Vaibhav, K., Kumar, A., & Srikantaswamy, R. (2020). Performance analysis of Brain Tumour Image Classification using CNN and SVM. Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020, 537–542. https://doi.org/10.1109/ICIRCA48905.2020.9183023
Bekhet, S., Alghamdi, A. M., & Taj-Eddin, I. (2022). Gender recognition from unconstrained selfie images: a convolutional neural network approach. International Journal of Electrical and Computer Engineering, 12(2), 2066–2078. https://doi.org/10.11591/ijece.v12i2.pp2066-2078
Chahal, P. K., Pandey, S., & Goel, S. (2020). A survey on brain tumor detection techniques for MR images. Multimedia Tools and Applications, 79(29–30), 21771–21814. https://doi.org/10.1007/s11042-020-08898-3
D, A. (2019). Face Recognition using Machine Learning Algorithms. Journal of Mechanics of Continua and Mathematical Sciences, 14(3). https://doi.org/10.26782/jmcms.2019.06.00017
Das, S., Aranya, O. F. M. R. R., & Labiba, N. N. (2019). Brain Tumor Classification Using Convolutional Neural Network. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019, May 2019, 1–6. https://doi.org/10.1109/ICASERT.2019.8934603
Deng, H., Zhang, W. X., & Liang, Z. F. (2021). Application of BP Neural Network and Convolutional Neural Network (CNN) in Bearing Fault Diagnosis. IOP Conference Series: Materials Science and Engineering, 1043(4), 1–10. https://doi.org/10.1088/1757-899X/1043/4/042026
Hadinata, P. N., Simanta, D., Eddy, L., & Nagai, K. (2023). applied sciences Multiclass Segmentation of Concrete Surface Damages Using.
Hasan, M. M., Ali, H., Hossain, M. F., & Abujar, S. (2020). Preprocessing of Continuous Bengali Speech for Feature Extraction. 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020, 1–4. https://doi.org/10.1109/ICCCNT49239.2020.9225469
Ismael, M. R., & Abdel-Qader, I. (2018). Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network. IEEE International Conference on Electro Information Technology, 2018-May, 252–257. https://doi.org/10.1109/EIT.2018.8500308
Le, D. N., Parvathy, V. S., Gupta, D., Khanna, A., Rodrigues, J. J. P. C., & Shankar, K. (2021). IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification. International Journal of Machine Learning and Cybernetics, 12(11), 3235–3248. https://doi.org/10.1007/s13042-020-01248-7
Li, Q., Liu, X., He, Y., Li, D., & Xue, J. (2023). Temperature guided network for 3D joint segmentation of the pancreas and tumors. Neural Networks, 157, 387–403. https://doi.org/10.1016/j.neunet.2022.10.026
Meranggi, D. G. T., Yudistira, N., & Sari, Y. A. (2022). Batik Classification Using Convolutional Neural Network with Data Improvements. International Journal on Informatics Visualization, 6(1), 6–11. https://doi.org/10.30630/joiv.6.1.716
Niharmine, L., Outtaj, B., & Azouaoui, A. (2022). Tifinagh handwritten character recognition using optimized convolutional neural network. International Journal of Electrical and Computer Engineering, 12(4), 4164–4171. https://doi.org/10.11591/ijece.v12i4.pp4164-4171
Pashaei, A., Sajedi, H., & Jazayeri, N. (2018). Brain tumor classification via convolutional neural network and extreme learning machines. 2018 8th International Conference on Computer and Knowledge Engineering, ICCKE 2018, Iccke, 314–319. https://doi.org/10.1109/ICCKE.2018.8566571
Saifan, R., & Jubair, F. (2022). Six skin diseases classification using deep convolutional neural network. International Journal of Electrical and Computer Engineering, 12(3), 3072–3082. https://doi.org/10.11591/ijece.v12i3.pp3072-3082
Samosir, R. S., Abdurachman, E., Gaol, F. L., & Sabarguna, B. S. (2022). Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm. IAES International Journal of Artificial Intelligence, 11(4), 1373–1383. https://doi.org/10.11591/ijai.v11.i4.pp1373-1383
Seetha, J., & Raja, S. S. (2018). Brain tumor classification using Convolutional Neural Networks. Biomedical and Pharmacology Journal, 11(3), 1457–1461. https://doi.org/10.13005/bpj/1511
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14, 4–15. https://doi.org/10.1109/RBME.2020.2987975
Takahashi, M., Miki, S., Fujimoto, K., Fukuoka, K., Matsushita, Y., Maida, Y., Yasukawa, M., Hayashi, M., Shinkyo, R., Kikuchi, K., Mukasa, A., Nishikawa, R., Tamura, K., Narita, Y., Hamada, A., Masutomi, K., & Ichimura, K. (2019). Eribulin penetrates brain tumor tissue and prolongs survival of mice harboring intracerebral glioblastoma xenografts. Cancer Science, 110(7), 2247–2257. https://doi.org/10.1111/cas.14067
Tashtoush, Y., Obeidat, R., Al-Shorman, A., Darwish, O., Al-Ramahi, M., & Darweesh, D. (2023). Enhanced convolutional neural network for non-small cell lung cancer classification. International Journal of Electrical and Computer Engineering (IJECE), 13(1), 1024. https://doi.org/10.11591/ijece.v13i1.pp1024-1038
Xie, Y., Zaccagna, F., Rundo, L., Testa, C., Agati, R., Lodi, R., Manners, D. N., & Tonon, C. (2022). Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives. Diagnostics, 12(8). https://doi.org/10.3390/diagnostics12081850
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018). A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis, 43, 98–111. https://doi.org/10.1016/j.media.2017.10.002