Abstract
Automated brain tumor detection is an important application in the medical field. There is a lot of methods developed for this task. In this paper, we have implemented an algorithm which detects the type of brain tumor from MRI image using supervised classification techniques. The major part of the work includes feature extraction using DWT and then reduction of features by using PCA. These reduced features are submitted to different classifiers like SVM, k-NN, Naïve Bayes and LDA. The results from each classifier are then submitted to a voting algorithm that chooses the most frequent result. The dataset for training contains 160 MRI images. The algorithm is processed on 200 * 200 images to reduce processing time. This method is tested and found to be much beneficial and rapid. It could be utilized in the field of MRI classification and can assist doctors to detect the tumor type and diagnose about patient abnormality level.
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Qureshi, A., Khan, K.B., Haider, H.A., Khawaja, R., Yousuf, M. (2020). An Ensemble Classification-Based Methodology Applied to MRI Brain Images for Tumor Detection and Segmentation. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_52
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DOI: https://doi.org/10.1007/978-981-15-5232-8_52
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