Artificial Intelligence Based Brain Tumor Localization Using YOLOv5
Keywords:
Brain Tumor Detection, Artificial Intelligence, YOLOv5
Abstract
Brain tumor is a mutation in the brain cells in which the cells keep dividing. The earlier the tumor detected, the higher survival rate for the patient. This study develops the brain tumor detection system by utilizing the you only look once (YOLO). The model is based on YOLOv5 architect. The open dataset of tumorous images is utilized. From this dataset, the corresponding masks are given alongside the images. Our study tries to compare several YOLOv5 models to localize the brain tumor. The results show YOLOv5m, YOLOv5l, and YOLOv5x models have higher precision and recall values. The inference time from those models is relatively small for recent computational resources. In conclusion, the YOLOv5 models have produced superior result in localizing the brain tumor
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References
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Vargo, M. M. (2017). Brain tumors and metastases. Physical Medicine and Rehabilitation Clinics of North America, 28(1), 115–141. https://doi.org/10.1016/j.pmr.2016.08.005
Bian, S., Repic, M., Guo, Z., Kavirayani, A., Burkard, T., Bagley, J. A., Krauditsch, C., & Knoblich, J. A. (2018). Genetically engineered cerebral organoids model brain tumor formation. Nature Methods, 15(8), 631–639. https://doi.org/10.1038/s41592-018-0070-7
Bortoff, G. A., Chen, M. Y., Ott, D. J., Wolfman, N. T., & Routh, W. D. (2000). Gallbladder stones: imaging and intervention. Radiographics, 20(3), 751-766. https://doi.org/10.1148/radiographics.20.3.g00ma16751
Blumlein, S. A. N. M. B. T. E., Bouchard, A., Schiller, N. B., Dae, M., Byrd 3rd, B. F., Ports, T., & Botvinick, E. H. (1986). Quantitation of mitral regurgitation by Doppler echocardiography. Circulation, 74(2), 306-314. https://doi.org/10.1161/01.CIR.74.2.306
Boltwood, C. M., Tei, C., Wong, M. A. Y. L. E. N. E., & Shah, P. M. (1983). Quantitative echocardiography of the mitral complex in dilated cardiomyopathy: the mechanism of functional mitral regurgitation. Circulation, 68(3), 498-508. https://doi.org/10.1161/01.CIR.68.3.498
Cheng, J (2017). Brain tumor dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.1512427.v5
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., ... & Feng, Q. (2015). Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one, 10(10), e0140381. https://doi.org/10.1371/journal.pone.0140381
Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., ... & Chen, W. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PloS one, 11(6), e0157112. https://doi.org/10.1371/journal.pone.0157112
Devunooru, S., Alsadoon, A., Chandana, P. W. C., & Beg, A. (2021). Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy. Journal of Ambient Intelligence and Humanized Computing, 12(1), 455-483. https://doi.org/10.1007/s12652-020-01998-w
Duran-Vega, M. A., Gonzalez-Mendoza, M., Chang-Fernandez, L., & Suarez-Ramirez, C. D. (2021). TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video. arXiv preprint arXiv:2111.08867. https://doi.org/10.48550/arXiv.2111.08867
El-Dahshan, E.-S. A., Mohsen, H. M., Revett, K., & Salem, A.-B. M. (2014). Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications, 41(11), 5526–5545. https://doi.org/10.1016/j.eswa.2014.01.021
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Fradkov, A. L. (2020). Early history of machine learning. IFAC-PapersOnLine, 53(2), 1385–1390. https://doi.org/10.1016/j.ifacol.2020.12.1888
Hamzenejadi, M. H., & Mohseni, H. (2022, November). Real-Time Vehicle Detection and Classification in UAV imagery Using Improved YOLOv5. In 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 231-236). IEEE. doi: 10.1109/ICCKE57176.2022.9960099.
Heim, B., Krismer, F., De Marzi, R., & Seppi, K. (2017). Magnetic resonance imaging for the diagnosis of Parkinson’s disease. Journal of neural transmission, 124(8), 915-964. DOI: 10.1007/s00702-017-1717-8
Hu, Y.-H. F., Ali, A., Hsieh, C.-C. G., & Williams, A. (2019). Machine learning techniques for classifying malicious API calls and N-Grams in Kaggle data-set. 2019 SoutheastCon. doi: 10.1109/SoutheastCon42311.2019.9020353.
Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C. and Schwendicke, F., 2019. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports, 9(1), pp.1-6. https://doi.org/10.1038/s41598-019-44839-3
Li, S., Li, Y., Li, Y., Li, M., & Xu, X. (2021). YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection. IEEE Access, 9, 141861-141875. doi: 10.1109/ACCESS.2021.3120870.
Jung, H. K., & Choi, G. S. (2022). Improved yolov5: Efficient object detection using drone images under various conditions. Applied Sciences, 12(14), 7255. https://doi.org/10.3390/app12147255
Jocher, G. (2022, November 22). ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation. Zenodo. https://doi.org/10.5281/zenodo.7347926
Maschio, M. (2012). Brain tumor-related epilepsy. Current Neuropharmacology, 10(2), 124–133. https://doi.org/10.2174/157015912800604470
McNeill, K. A. (2016). Epidemiology of brain tumors. Neurologic Clinics, 34(4), 981–998. https://doi.org/10.1016/j.ncl.2016.06.014
Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020). A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access, 8, 55135–55144. https://doi.org/10.1109/access.2020.2978629
Oh, S. L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., & Acharya, U. R. (2020). A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Computing and Applications, 32(15), 10927-10933. https://doi.org/10.1007/s00521-018-3689-5
Ostrom, Q. T., Fahmideh, M. A., Cote, D. J., Muskens, I. S., Schraw, J. M., Scheurer, M. E., & Bondy, M. L. (2019). Risk factors for childhood and adult primary brain tumors. Neuro-Oncology, 21(11), 1357–1375. https://doi.org/10.1093/neuonc/noz123
Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in biology and medicine, 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
Patel, A. (2020). Benign vs malignant tumors. JAMA Oncology, 6(9). https://doi.org/10.1001/jamaoncol.2020.2592
Richter, A., Woernle, C. M., Krayenbühl, N., Kollias, S., & Bellut, D. (2015). Affective symptoms and white matter changes in brain tumor patients. World Neurosurgery, 84(4), 927–932. https://doi.org/10.1016/j.wneu.2015.05.031
Rickers, C., Wilke, N. M., Jerosch-Herold, M., Casey, S. A., Panse, P., Panse, N., ... & Maron, B. J. (2005). Utility of cardiac magnetic resonance imaging in the diagnosis of hypertrophic cardiomyopathy. Circulation, 112(6), 855-861. https://doi.org/10.1161/CIRCULATIONAHA.104.507723
Sadrawi, M., Lin, Y. T., Lin, C. H., Mathunjwa, B., Hsin, H. T., Fan, S. Z., ... & Shieh, J. S. (2021). Non-invasive hemodynamics monitoring system based on electrocardiography via deep convolutional autoencoder. Sensors, 21(18), 6264. https://doi.org/10.3390/s21186264
Selby, N. M., Blankestijn, P. J., Boor, P., Combe, C., Eckardt, K. U., Eikefjord, E., ... & Sourbron, S. (2018). Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA. Nephrology Dialysis Transplantation, 33(suppl_2), ii4-ii14. DOI: 10.1093/ndt/gfy152
Vargo, M. M. (2017). Brain tumors and metastases. Physical Medicine and Rehabilitation Clinics of North America, 28(1), 115–141. https://doi.org/10.1016/j.pmr.2016.08.005
Published
2023-03-24
How to Cite
Sadrawi, M., Fugaha, D., Heerlie, D., Lorell, J., Gautama, N., & Aminuddin, M. (2023). Artificial Intelligence Based Brain Tumor Localization Using YOLOv5. Indonesian Journal of Life Sciences, 5(01), 1-9. https://doi.org/https://doi.org/10.54250/ijls.v5i01.176
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Section
Information Technology in Life Sciences
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