Classification of MRI Images for Brain Tumor Patient Screening
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Abstract
This research aims to create a model for screening brain tumor patients using MRI imaging data from www.kaggle.com. The data was gathered by Chakrabarty (2021) with a total of 253 images. The data was analyzed using the cross-industry standard process for data mining, then the performances of the classification models were compared. The results showed that random forest technique gave the best result for predicting the likelihood of brain tumors, with an accuracy of 76.31%. The F-measure was 73.48% with a sensitivity of 70.14% and a specificity of 82.69%. The data analysis results could be utilized to develop an information system for future patient screenings for brain tumors.
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References
Armstrong, T. S., Vera-Bolanos, E., Acquaye, A. A., Gilbert, M. R., Ladha, H., & Mendoza, T. (2016). The symptom burden of primary brain tumors: evidence for a core set of tumor- and treatment-related symptoms. Neuro-Oncology, 18(2), 252–260. https://doi.org/10.1093/neuonc/nov166
Back, M., Back, E., Kastelan, M., & Wheeler, H. (2014). Cognitive deficits in primary brain tumours: A framework for management and rehabilitation. Journal of Cancer Therapy, 5(1), 74-81. https://doi.org/10.4236/jct.2014.51010
Bunevicius, A., Deltuva, V., Tamasauskas, S., Tamasauskas, A., Laws, E.R., Jr, & Bunevicius, R. (2013). Low triiodothyronine syndrome as a predictor of poor outcomes in patients undergoing brain tumor surgery: A pilot study. Journal of Neurosurgery, 118(6), 1279–1287. https://doi.org/10.3171/2013.1.JNS121696
Bunevicius, A., Tamasauskas, S., Deltuva, V., Tamasauskas, A., Radziunas, A., & Bunevicius, R. (2014). Predictors of health-related quality of life in neurosurgical brain tumor patients: focus on patient-centered perspective. Acta Neurochirurgica, 156, 367–374. https://doi.org/10.1007/s00701-013-1930-7
Cai, L., Gao, J., & Zhao, D. (2020). A review of the application of deep learning in medical image classification and segmentation. Annals of Translational Medicine. 8(11), 713. https://doi.org/10.21037/atm.2020.02.44
Chakrabarty, N. (2021). Brain MRI images for brain tumor detection. Retrieved 8 December 2022. Retrieved from https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection
Johnson, D.R., Sawyer, A.M., Meyers, C.A., O’Neill, B.P., & Wefel, J.S. (2012). Early measures of cognitive function predict survival in patients with newly diagnosed glioblastoma. Neuro-Oncology, 14(6), 808-816. https://doi.org/10.1093/neuonc/nos082
Klaythong, T. & Srisawat, C. (2023). Forecasting dropout of undergraduates Pibulsongkram Rajabhat University with data mining technique. Journal of Applied Informatics and Technology, 5(1), 1-17. https://doi.org/10.14456/jait.2023.1 [In Thai]
Kumjit, K., Jaikoomkao, D., Phumirang, W., Sattanako, A., & Sukpraser, A. (2022). The efficiency of data mining technique for the prognosis of cerebrovascular disease. Journal of Applied Informatics and Technology, 4(2), 87-98. https://doi.org/10.14456/jait.2022.7 [In Thai]
Lapthanachai, N., Chomthong, A., Waijanya, S., & Promrit, N. (2023). Classification of nail abnormalities using convolutional neural network. Journal of Applied Informatics and Technology, 5(1), 18–35. https://doi.org/10.14456/jait.2023.2 [In Thai]
Mehdy, M.M., Ng, P.Y., Shair, E. F., Md Saleh, N.I., & Gomes, C. (2017). Artificial neural networks in image processing for early detection of breast cancer. Computational and Mathematical Methods in Medicine, 2017, 1-15. https://doi.org/10.1155/2017/2610628
Raza, S. (2021). Brain tumor detector. Retrieved 8 December 2022. Retrieved from https://www.kaggle.com/code/saharraza/preprocessing/notebook
Saisangchan, U., Chamchong, R., & Suwannasa, A. (2022). Analysis of lime leaf dis- ease using deep learning. Journal of Applied Informatics and Technology, 4(1), 71-86. https://doi.org/10.14456/jait.2022.6 [In Thai]
Siegel, R.L., Miller, K.D., & Jemal, A. (2018). Cancer statistics, 2018. CA: A Cancer Journal for Clinicians. 68(1), 7-30. https://doi.org/10.3322/caac.21442
Sikarin. (2022).ปวดหัวบ่อย เห็นภาพซ้อน สัญญาณเตือนเสี่ยงเนื้องอกในสมอง [Frequent headaches, double vision, warning signs of brain tumor]. Retrieved 8 December 2022. Retrieved from https://www.sikarin.com/health/ เนื้องอกในสมอง-brain-tumor [In Thai]
Silva, D. (2021). Brain tumor detector. Retrieved 9 December 2022. Retrieved from https://www.kaggle.com/code/diegosilvadefrana/brain-tumor-detector/notebook
Songhmokholongtun. (2018). Central Limit Theorem (CLT) สำหรับนักลงทุน [Central Limit Theorem (CLT) for investors]. Retrieved from 25 November 2022. Retrieved from https://songhmok-holongtun.com [In Thai]
Srisubat, A. et al. (2017). Effectiveness of gamma knife for patients with brain tumor: A systematic review. Journal of the Department of Medical Services. 42(4), 64-69. http://www.imrta.dms.moph.go.th/imrta/images/65-70%20From4.pdf [In Thai]
Sukprasert, A. (2021). คู่มือการทำเหมืองข้อมูลด้วยโปรแกรม RapidMiner Studio [Guide to mining with RapidMiner Studio]. Mahasakham : Mahasarakham Business School, Mahasarakham University. [In Thai]
Tankumpuan, T., Utriyaprasit, K., Chayaput, P., & Itthimathin, P. (2015). Predictors of physical functioning in postoperative brain tumor patients. The Journal of Neuroscience Nursing, 47(1), E11-E21. https://doi.org/10.1097/JNN.0000000000000113
Yang, X. et al. (2017). Pseudo CT estimation from MRI using patch-based random forest. Proceedings of SPIE—the International Society for Optical Engineering, 10133, 101332Q. https://doi.org/10.1117/12.2253936
Yang, X., Islam, M.S., & Khaled, A.M. (2019). Functional connectivity magnetic resonance imaging classification of autism spectrum disorder using the multisite ABIDE dataset, IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 2019, pp. 1-4. https://doi.org/10.1109/BHI.2019.8834653