Abstract
The paper aims to provide a comparative study on the detection and classification of brain tumors (BT) using different machine learning algorithms. In the process, different popular and commonly BT image data sets such as the BRATS, OASIS, and the NBTR have been used for the said purpose. The pre-processed BT images are enhanced using the filtering approach and then segmented using the fuzzy C-means (FCM) algorithm for the extraction of suitable and reliable features. The multi-resolution capability of wavelet transform (WT) has been explored to extract the detailed coefficients for simulation of the chosen classifiers. The recognition accuracy of the classification algorithms such as the K-nearest neighbor (KNN), decision tree (DT), neural network (NN), discriminant analyzer (DA), support vector machine, and Naive Bays’ (NB) have been compared for their applicability in classifying BT images. The highest average recognition accuracy of 96.4% has been reported with the KNN algorithms for the OASIS data set as revealed from our results.
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Sahoo, L., Sarangi, L., Dash, B.R., Palo, H.K. (2020). Detection and Classification of Brain Tumor Using Magnetic Resonance Images. In: Pradhan, G., Morris, S., Nayak, N. (eds) Advances in Electrical Control and Signal Systems. Lecture Notes in Electrical Engineering, vol 665. Springer, Singapore. https://doi.org/10.1007/978-981-15-5262-5_31
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