Feature extraction and selection with optimization technique for brain tumor detection from MR images

Abstract

Brain tumor detection and extraction within the time frame to offer better healthcare is vital and very important, but a time-consuming task performed by clinical supervisors or radiologists. Its accuracy for the brain tumor detection from modern imaging modalities also depends on their experience only. So the use of computer-aided methodology is very important to overcome these limitations. Our proposed technique uses feature extraction and optimization of the extracted features based on their relevance to detect brain tumor from the magnetic resonance images. By optimizing extracted features, only relevant features are retained for further analysis and so reduce the mathematical complexity of classification of the brain tumor and so detect the abnormalities at a fast rate with higher accuracy as compared to manual detection. The experimental results perform on the different brain magnetic resonance images obtained an average of 0.73 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists.