## 2 Cad Systems of Brain Disorders Based on MRI Technology
### Classification of Brain Images
#### 2.3.1 Classification Techniques Proposed in Literature: Description, Advantages and Disadvantages
\begin{table}
\begin{tabular}{c l l l l l l} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Classification Techniques**} & \multirow{2}{*}{**Database**} & \multicolumn{3}{c}{**Performance Measures (\%)**} \\ \cline{3-7}  & & & **AC** & **SE** & **SP** \\ \hline \multirow{3}{*}{[150]} & Feature extraction: Random forest & \multirow{3}{*}{144 AD and 189 HC subjects} & \multirow{3}{*}{AD: 0.97} & \multirow{3}{*}{89} & \multirow{3}{*}{94} \\  & Classification: SVM (bootstrap estimation and 20-fold CV) with supervised learning & & & & \\ \hline \multirow{3}{*}{[151]} & Classification: SVM (leave-one-out CV) with & \multirow{3}{*}{37 AD and 40 HC subjects.} & \multirow{3}{*}{AD:96.1} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & supervised learning & & & & \\ \hline \multirow{3}{*}{[152]} & Classification: SVM, Bayes statistics, VFI with & \multirow{3}{*}{32 AD, 24 MCI and 18 HC subjects} & \multirow{3}{*}{AD: 92 MCI-c 75} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & supervised learning & & & & \\ \hline \multirow{3}{*}{[153]} & Feature selection: Pearson’s correlation & \multirow{3}{*}{20 AD and 25 HC subjects from Hospital de Santiago} & \multirow{3}{*}{AD: 100} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & Classification: SVM (linear basis kernel and & & & & \\ \cline{1-1}  & leave-one-out CV) with supervised learning & & & & \\ \hline \multirow{3}{*}{[154]} & Features extraction: MBL & \multirow{3}{*}{ADNI: 198 AD, 238 S-MCI, 167 progresses MCI and 231} & \multirow{3}{*}{AD:SVM: 86} & \multirow{3}{*}{94} & \multirow{3}{*}{78} \\  & Classification: LDA, SVM with supervised learning & & & & \\ \cline{1-1}  & Feature extraction: SIFT & \multirow{3}{*}{100 AD and 98 HC subjects.} & \multirow{3}{*}{AD: 86} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & Segmentation: k-means & & & & \\ \cline{1-1}  & Classification: SVM (leave-one-out CV) with & & & & \\ \cline{1-1}  & supervised learning & & & & \\ \hline \multirow{3}{*}{[156]} & Feature extraction: fractal analysis & \multirow{3}{*}{13 AD and 10 HC subjects.} & \multirow{3}{*}{AD: 100} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & Classification: SVM (quadratic kernel) with & & & & \\ \cline{1-1}  & supervised learning & & & & \\ \hline \multirow{3}{*}{[7]} & Segmentation: Hybrid FCM/PCM & \multirow{3}{*}{45 AD and 50 HC subjects from ADNI phantom with } & \multirow{3}{*}{AD: MRI: 75} & \multirow{3}{*}{84.87} & \multirow{3}{*}{81.58} \\  & Classification: SVM (RBF kernel and & & & & \\ \cline{1-1}  & leave-one-out CV) with supervised learning & & & & \\ \hline \multirow{3}{*}{PMAD: p-mild AD, SVM: Support vector machines, FTLD: frontotemporal lobar degeneration, HC: Healthy control, AD: Alzheimer’s disease, ADNI: Alzheimer disease neuroimaging, P-MCI: Probable and cognitive impairment, SMCI: Stable MLCV: Cross-validation, SVM: Statistical Parametric Mapping, SOM: Self-organizing maps, RBF: Radial basis function, VFI: voting feature intervals, PCA: Principal component analysis, BPNN: back propagation neural network, ADC: Area under curve, MBI: Manifold-based learning, LDA: Linear discriminant analysis, SIFT: Scale-invariant feature transforms, FCM: Fuzzy C-means, PCM: Possible C-means algorithm. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Techniques based on Supervised Learning  
* **Artificial neural networks:** ANNs are used in many works related to neuroimaging as classification method [157, 158, 159, 160, 161, 162, 163] have been widely applied as a classifier to distinguish new test data. They are universal functional approximations allowing to approximate any function with arbitrary precision. They are flexible nonlinear models for modeling complex real-world applications. They are self-adapting adaptable to data, without explicit specification of the functional or distributional form with the underlying model. They are able to estimate the later probabilities, necessary to establish classification rules and statistical analyzes.