A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy |
| |
| Authors: | Yudong Zhang Preetha Phillips Ge Liu Xingxing Zhou Shuihua Wang |
| |
| Institution: | 1.School of Computer Science and Technology,Nanjing Normal University,Nanjing,China;2.Key Laboratory of Statistical Information Technology and Data Mining,State Statistics Bureau,Chengdu,China;3.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry Of Education,Jilin University,Changchun,China;4.State Key Lab of CAD & CG,Zhejiang University,Hangzhou,China;5.Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing,Nanjing,China;6.State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing,China;7.School of Natural Sciences and Mathematics,Shepherd University,Shepherdstown,USA;8.Department of Psychiatry, College of Physicians, & Surgeons,Columbia University,New York,USA |
| |
| Abstract: | This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods. |
| |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ |
|
