## 6 Conclusions and Future Research  
We proposed a novel CAD system for pathological brain detection (PBD) using DTCWT, VE, and TSVM. The results show that the proposed method yields better results than 12 existing methods in terms of classification accuracy.  
Our contributions include three points: (1) We investigate the potential use of dual-tree complex wavelet transform (DTCWT) in MR image classification, and prove DTCWT is effective; (2) We utilize twin support vector machine (TSVM) and prove it is better than canonical SVM and GEPSVM; (3) The proposed system \"DTCWT + VE + TSVM\" is superior to nineteen state-of-the-art systems.  
The limitation of our method is the dataset size is too small. We will try to re-check our methods by creating a large dataset. Another limitation is the dataset cannot reflect real-word scenario, thus we  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Neuoradiologist** & **Accuracy** \\ \hline O1 & 74\% \\ O2 & 78\% \\ O3 & 77\% \\ O4 & 79\% \\ Our method & 96\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison to human reported results.  
need to obtain more data from hospitals directly in the future. The third limitation is that our data involves only middle and late stage of diseases; hence, our method performs not so good for MR images with diseases in early stage.  
In the future, we will consider to validate our method use real clinical data and use advanced classification methods, such as RBFNN, deep leaning, least-square techniques. Besides, we will try to apply our method to remote-sensing related fields or hearing loss detection [58]. The advanced parameter estimation, case-based reasoning [59], and optimization [60] techniques will be carried out in a thorough way. Fuzzy method [61] may be applied to remove outliers in the dataset. Coarse-graining [62] can help extract more efficient entropy that is robust to the noises. Video-on-demand [63; 64] services may be applied to help reduce computation resources. Particularly, we shall acquire more datasets and compare our method with human interpretation.  
This paper was supported by Natural Science Foundation of Jiangsu Province (BK20150983), Open Fund of Key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University (93K172016K17), Open Fund of Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, (SDL201608), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Nanjing Normal University Research Foundation for Talented Scholars (2013119 XGQ0061, 2014119 XGQ0080), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (15-140-30-008K), Open Project Program of the State Key Lab of CAD & CG, Zhejiang University (A1616), Fundamental Research Funds for the Central Universities (LGFB201604).  
[PERSON] & [PERSON] conceived the study. [PERSON] designed the model. [PERSON] acquired the data. [PERSON] & [PERSON] analyzed the data. [PERSON] interpreted the data. [PERSON] developed the program. [PERSON] wrote the draft. All authors gave critical revisions and approved the submission.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
(A)(BP)(F)(PC)NN (Artificial) (Back-propagation) (Feed-forward) (Pulse-coupled) neural network  
(B)PSO(-MT) (Binary) Particle Swarm Optimization (-Mutation and TVAC)  
(D)(S)W(P)T (Discrete) (Stationary) wavelet (packet) transform  
(k)(F)(LS)(GEP)SVM (kernel) (Fuzzy) (Least-Squares) (Generalized eigenvalue proximal) Support vector machine  
(W)(P)(T)E (Wavelet) (Packet) (Tsallis) entropy  
CAD (Computer-aided diagnosis  
CS (Cost-sensitivity)  
FFFE (Fractional Fourier entropy  
HMI (Hu moment invariant  
KNN (K-nearest neighbors  
MR(I) (Magnetic resonance (imaging)  
PCA (Principal Component Analysis  
RBF (Radial Basis Function  
TVAC (Time-varying Acceleration Coefficients  
WTT (Welch's \(t\)-test