Abstract

In medical image processing, segmentation is an important and challenging task. It is classically used to identify object contours and extract the object from the image. Tumor Classification is an significant in medical image analysis since it provides information related to anatomical structures as well as possible anomalous tissues necessary to treatment planning and patient follow-up. In this study, a new approach for automatic classification of brain tumor in enhanced MRI images is developed. Our proposed method consists of Five steps: i) Preprocessing ii) Tumor Region Segmentation iii) Feature Extraction using Wavelet and Level set method iv) Feature Selection and v) Feature Classification using Ada-Boost classifier. The experimental results are validated using the evaluation metrics such as sensitivity, specificity and accuracy. Our proposed system experimental results are compared to other neural network based classifier such as Feed Forward Neural Network (FFNN) and Radial Basics Function (RBF). The classification accuracy of proposed method produces better results compared to other leading tumor classification methods.

Keywords:

Classification, DWT, feature extraction, MRI, PCA, segmentation, tumor,

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Competing interests

The authors have no competing interests.

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