Abstract
Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect on how the network perform when predicting cases of brain tumor, contrast accounted for 64.3 %, correlation accounted for 56.7 %, and entropy accounted for 54.8 %. All remaining characteristics accounted for 21.3-46.8 % of normalized importance. The output of the neural networks showed that sensitivity and specificity were scored remarkably high level of probability as it approached % 96.
Highlights
Brain tumor is a medical issue that harvest according to the American brain tumor association [1] thousands of lives every year
For this reason the information of the thirteen features listed in Table 2 were subjected to a multilayer perceptron artificial neural network
The output of the image processing software designed by Zhang et al [11] were used as an input to the Artificial Neural Networks (ANN) multilayer perceptron discriminant function
Summary
Brain tumor is a medical issue that harvest according to the American brain tumor association [1] thousands of lives every year. In addition to the investigation of the MRI images, shape and texture characteristics of the images were subjected to different statistical techniques in order to provide valuable information about characteristics that best describe the type of tumor In this context Neelam Marshkole et al [10], used texture and shape features of MRI images to classify brain tumors to either malignant or benign using linear vector quantization technique. They found these features very effective in the process of classification.
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