A pathological brain detection system based on kernel based ELM

作者：Lu, Siyuan; Lu, Zhihai; Yang, Jianfei; Yang, Ming; Wang, Shuihua^*

来源：Multimedia Tools and Applications, 2018, 77(3): 3715-3728.

DOI：10.1007/s11042-016-3559-z

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

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 x 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.

• 出版日期2018-2
• 单位南京师范大学; 浙江大学; 南京医科大学