A pathological brain detection system based on kernel based ELM

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Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for diagnosis to classify MR images of brain into healthy or abnormal automatically and accurately, since the information set MRIs generate is too large to interpret with manual methods. We propose a new approach with wavelet-entropy as the features and the kernel based extreme learning machine (K-ELM) to be the classifier. Our method employs 2D-discreet wavelet transform (DWT), and calculates the entropy as features. Then, a K-ELM is trained to classify images as pathological or healthy. A 10 × 10-fold cross validation is conducted to prevent overfitting. The method achieves the sensitivity as 97.48 %, the specificity as 94.44 %, and the overall accuracy as 97.04 % based on 125 MR images. The performance suggests the classifier is robust and effective by comparison with the recently published approaches.

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This study is financially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Natural Science Foundation of Jiangsu Province (BK20150983), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), the Fundamental Research Funds for the Central Universities (LGYB201604)

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Correspondence to Shuihua Wang.

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The authors declare no conflict of interest involved in this paper.

Additional information

Siyuan Lu, Zhihai Lu and Jianfei Yang contributed equally to this work.

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Lu, S., Lu, Z., Yang, J. et al. A pathological brain detection system based on kernel based ELM. Multimed Tools Appl 77, 3715–3728 (2018).

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  • Wavelet entropy
  • K-ELM
  • Classification
  • Pattern recognition