## 5 Discussion
The most important classes of architecture include fully connected multilayer perceptrons, deep convolutional networks with many convolution filters, recurrent networks that examine patterns in data over space or time, long short-term memory networks that take advantage of \"training example experience\", autoencoders that can reconstruct data to an accuracy that varies based on the quality of their training data, and adversarial networks that attempt to fool one another in discriminating between real and synthetic data [135; 136; 138; 139; 140; 162; 163; 164; 165]. Like other forms of machine learning, deep learning methods such as NN are also prone to the problem of overfitting; however, many \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Author/Reference** & **Methodology** & **Task** & **Performance** \\ \hline \multirow{6}{*}{[PERSON] et al. [141]} & \multirow{6}{*}{NN—RNN} & AD vs. HC & SENS = 91.4\% \\  & & & SPEC = 91\% \\  & & & ACC = 91.2\% \\  & & AD vs. HC & AUC = 953 \\  & & MCI vs. HC & SENS = 78.1\% \\  & & & SPEC = 80\% \\  & & & ACC = 78.9\% \\  & & & AUC = 0.839 \\ \hline \multirow{2}{*}{[PERSON] et al. [143]} & \multirow{2}{*}{NN—CNN} & AD identification & AUC = 0.93 \\  & & MCI identification & AUC = 0.63 \\  & & non-AD/non-MCI identification & AUC = 0.73 \\ \hline [PERSON] et al. [144] & NN—Graph CNN & AD vs. HC & ACC = 93\% \\  & & AD vs. MC1 vs. HC & ACC = 77\% \\ \hline [PERSON] \& [PERSON] [145] & NN—3D CNN & AD vs. HC & ACC = 93\% \\  & & pMCI vs. MCI & ACC = 84.2\% \\ \hline \multirow{3}{*}{[PERSON] et al. [146]} & \multirow{3}{*}{NN—Residual CNN} & AD vs. HC & ACC = 93.5\% \\  & & & AUC = 0.96 \\  & & pMCI vs. MCI & ACC = 74.7\% \\  & & AUC = 0.811 \\ \hline [PERSON] et al. [147] & NN—Pyramidal CNN & MCI to AD conversion & ACC = 83.0\% \\ \hline \multirow{3}{*}{[PERSON] et al. [148]} & \multirow{3}{*}{NN—3D CNN} & DLIB identification & AUC = 0.962 \\  & & AD identification & AUC = 0.964 \\  & & MCI identification & AUC = 0.714 \\ \hline [PERSON] et al. [150] & NN—3D CNN & MCI to AD conversion & AUC = 0.89 \\ \hline \multirow{6}{*}{[PERSON] et al. [152]} & \multirow{3}{*}{NN—3D CNN} & SENS = 95.4\% \\  & & & SPEC = 96.9\% \\  & & & ACC = 96.2\% \\  & & MCI vs. HC & SENS = 48.2\% \\  & & & SPEC = 82.4\% \\  & & & ACC = 64.2\% \\ \hline [PERSON] et al. [154] & NN—Multi-scale CNN & pMCI vs. SMC1 & ACC = 82.51\% \\ \hline [PERSON] et al. [155] & NN—SVM-CNN & MCI to AD conversion & ACC = 86.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._strategies exist to counteract this, such as batch normalization, regularization, dropout, drop-connect, and early stopping methods [166; 167; 168]. Perhaps the most significant advantage of NN as a method is the lack of feature engineering. Whereas traditional machine learning methods were forced to handcraft features for consideration by a computationally intelligent algorithm (e.g., SVM), an NN with sufficient capacity is capable of extracting features without any special consideration and even of performing complicated tasks such as image segmentation or preprocessing operations [169; 170; 171; 172; 173; 174; 175]. NNs may also serve as feature extractors, with the output being fed into another classifier like an SVM [155]. One caveat is the lack of neural network studies that validate themselves against multiple forms of neurodegenerative dementia disorders, which are now commonly performed in models based on PCA or SSM methods [116; 121; 122]. This is important considering post-mortem studies which show AD diagnosis to be quite nonspecific and increase the cost of care [2; 3; 4; 176]. Another is the demographic homogeneity of the subject population of the routinely used ADNI dataset [177]. This is being addressed through the increasing availability of online neuroimaging datasets, such as the creation of the Japanese Alzheimer's Disease Neuroimaging Initiative (JADNI) and the Worldwide Alzheimer's Disease Neuroimaging Initiative (WADNI), and several European neuroimaging initiatives [178; 179; 180; 177; 178].