Abstract
Brain tumor characterizes the aggregation of abnormal cells in specific tissues of the brain zone. The prior distinguishing proof of brain tumors has a huge influence on the treatment and recovery of the patient. The identification of a brain tumor and its evaluation is commonly a troublesome and tedious assignment. For effective classification and grading of brain tumor images, in this paper, we present an automatic MRI brain tumor classification system. The proposed work consists of four modules namely, pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in the input image is removed using the Median Filter because the noises present in the input images will affect the accuracy of the classification process. At once, the images are converted into 3 × 3 blocks. Then, the texture features are extracted from the pre-processed image. After the feature extraction process, the features are given to the adaptive k-nearest neighbor classifier to classify an image as normal or abnormal. Later, the tumor regions are segmented with the help of the optimal possibilistic fuzzy C-means clustering algorithm. Both classification and the segmentation appearance technique are evaluated in terms of accuracy; sensitivity as well as specificity. For experimental analysis two dataset are utilized namely, BRATS MICCAI brain tumor dataset and publically available dataset.
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Kumar, D.M., Satyanarayana, D. & Prasad, M.N.G. MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier. J Ambient Intell Human Comput 12, 2867–2880 (2021). https://doi.org/10.1007/s12652-020-02444-7
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DOI: https://doi.org/10.1007/s12652-020-02444-7