## 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  
Below is an effort to bring together most of the classification techniques (see Figure 3) used for the diagnosis of AD, emphasizing their advantages and disadvantages. Figure 3: MRI brain images classification methods described in the literature: In this work, the classification methods have been grouped into two categories (green color) according to the type of learning, namely: supervised and unsupervised learning. DNN: Deep neural network, CNN: Convolutional neural network, MLP: multi-layer perceptron, BPNN: Back propagation neural network, PCNN: Pulse-coupled neural network, SVM: Support vector machines, SVDD: Support vector data description, GMM: Gaussian mixture model, HMM: Hidden Markov models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Classification Techniques** & **Database** & \multicolumn{3}{c}{**Performance Measures (\%)**} \\ \cline{3-6}  & & Group 1: 20 AD and 20 HC, samples from the Rochester community, Minnesota, USA. & **AC** & **SE** & **SP** \\ \cline{3-6}
[143] & Classification: SVM (linear basis kernel) with & Group 2: 14 AD and 14 HC from the Dementia Research Center, Univ. College London, UK. & & AD: 96 & \\  & supervised learning & Group 3: 33 probable mild AD and 57 HC sample in & & PMAD: 89 & NA & NA \\  & & Rochester, Minnesota, USA. & & FFLD: 89 & & \\ \hline
[144] & Segmentation: hierarchical networks & ADNI: 100 P-MCI and 125 HC subjects & & & & \\  & Classification: SVM with supervised learning & [[http://adni.loni.usc.edu/](http://adni.loni.usc.edu/)]([http://adni.loni.usc.edu/](http://adni.loni.usc.edu/)) & & MCI: 84.35 & NA & NA \\ \hline
[145] & Classification: SVM with heavy-1-out CV and & 19 AD and 20 HC subjects & & AD: 80 & NA & NA \\  & 3-fold CV and supervised learning & & & & & \\ \hline
[146] & Segmentation: SPMS software from department of & 25 AD subjects (11 men, 14 women), 24 MCI subjects (10 & AD: 84 & 84 & 84 \\  & Imaging Neuroscience, London, UK and using & & men, 14 women) and 25 HC (13 men, 12 women) subjects & MCI: 73 & 75 & 70 \\ \hline
[147] & Feature extraction: Wavelet coefficients & & & & \\  & Classification: SOM neural network with & & AANLIB of Harvard Medical School: 46 AD and 6 HC & & & \\  & unsupervised learning & & subjects. & & AD:SOM: 94 & NA & NA \\  & SVM (linear, Polynomial, RBF basis kernel) with & & [[http://med.harvard.edu/AANLIB/](http://med.harvard.edu/AANLIB/)]([http://med.harvard.edu/AANLIB/](http://med.harvard.edu/AANLIB/)) & & & \\  & supervised learning & & & & \\ \hline
[148] & Feature extraction: Wavelet transform & & & & & \\  & Segmentation: PCA & & & & \\  & Classification: BPNN with supervised learning & & & & \\ \hline
[149] & Classification: SVM (bootstrap method) with & & & & \\  & supervised learning & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Some works described in the literature related to the computer aided-diagnosis systems of Alzheimer’s disease through MRI. These CAD systems used classification methods based on supervised or unsupervised training. The efficiency of the proposed CAD system is demonstrated by estimating the percentage of the following performance measures (last column): Sensitivity (SE) which represents the true positive rate, specificity (SP) which estimates the true negative rate and accuracy (AC) that determines the proportion of true results in the database, whether true positive or true negative. In [143], the reported learning time was around a week, while in [148] the computation time reported was 0.0451 s per image.