Abstract
A brain tumor is a life-threatening disease that can be diagnosed with medical imaging systems. The latter play significant roles in healthcare in assisting medical professionals in visualizing and localizing regions of the suspected tumor. An automatic system able to provide an accurate diagnosis for supporting radiologists’ interpretation of digital images is highly sought after. In this study, an intelligent system using Support Vector Machine (SVM) classifier for diagnosis of brain tumor is proposed. This study considered 140 Magnetic Resonance Imaging (MRI) images comprised of 70 normal and 70 abnormal images for investigations. To further improve the classifier’s efficiency, fivefold cross validation is performed to train and test the developed system rigorously. The results showed that the developed system achieved relatively good performance with accuracy, specificity, precision, and recall rate of 85.7%, 87.1%, 86.8%, and 84.3%, respectively. In conclusion, the performance of the developed system may be further improved by including more data, for example through the augmentation process in the training. This is in addition to different adaptability that may be required in preprocessing and classification.
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Acknowledgements
This research was supported by Ministry of Higher Education of Malaysia (MOHE) through Fundamental Research Grant Scheme (FRGS/1/2019/TK04/UTHM/03/10) and Universiti Tun Hussein Onn Malaysia.
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Ali, Z., Huong, A., Mahmud, W.M.H.W. (2022). Computer Aided System for Automatic Detection of Brain Tumor. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_55
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DOI: https://doi.org/10.1007/978-981-16-8515-6_55
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