Aim: It is beneficial to classify brain images as healthy or pathological automatically, because
3D brain images can generate so much information which is time consuming and tedious for
manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the
most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways
of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods;
however they suffer from low accuracy.
Method: Therefore, we proposed a novel approach which employed 2D discrete wavelet transform
(DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme
learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A
10x10-fold cross validation was performed to evaluate the out-of-sample performance.
Result: The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy
of 98.33% over 132 MR brain images.
Conclusion: The experimental results suggest that the proposed approach is accurate and robust in
pathological brain detection.