Abstract
Magnetic resonance imaging (MRI) belongs to an imaging modality that helps doctors in diagnosis. MRI is commonly used for brain because it can generate clearer images in soft tissues than CT and PET. Early detection of brain diseases is dependent on brain MRIs to a great extent. However, brain MRIs contain too much information, manual interpretation requires too much time of the doctors. Therefore, in this paper, we proposed a novel computer aided diagnosis (CAD) system that can classify brain MRIs to be pathological or healthy automatically and accurately. It can provide a second opinion for the doctors and improve their efficiency. Firstly, we extracted 7 Hu moment invariants and 7 wavelet entropies from each brain MRI to form the feature vector. Then, we employed three improved extreme learning machines'(ELM) as the classifier% they are kernel based ELM (K-ELM), bat algorithm optimized ELM (BA-ELM) and weighted ELM (W-ELM). Open dataset was used in experiment. The statistics were obtained using 10 x 10 fold cross validation. The results revealed that W-ELM performed the best among the three classifiers with sensitivity of 96.93%, specificity of 96.67% and overall accuracy of 96.89% in merely 0.0026 seconds. Therefore, our proposed method is effective for pathological brain detection.