# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 5 Diagnosis Results and Analysis  
### Convolution Neural Network Diagnostic Results  
#### 5.1.1 Single Modality Training Model  
By setting aside method, 3008 images were used as training sets, and 1534 images were used as evaluation set. After training and testing, the diagnostic accuracy used MRI only images of CNN reached 80.65% and the U-Net reached 84.17%. By using the similar method, results of training with PET only images were also achieved. Mean and SD of AUC shown as Table 4:  
In Table 5, AUC means area under the receiver operating characteristic curve. AUC represents the degree of differentiation of different categories, the value is proportional to the probability that the model is correctly classified. _ACC_ means accuracy, and its value is proportional to the overall prediction performance. _SENS_ means sensitivity. In _SENS_, the numerator is the number of positive samples predicted to be correct, and the denominator is the number of positive samples predicted to be negative, also known as the recall rate, indicating how many positive samples predicted to be correct. _SPEC_ means specificity, which is contrary to _SENS_. _SPEC_ indicates how many of the negative samples are predicted to be correct. _PPV_ means positive predict value, which is based on the result of prediction, and it represents the amount of the samples predicted to be positive were correct. There are two possible ways to predict a positive class, one is to predict a true positive (TP), and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CNN** & **DeU-Net** & **InceptionV3** & **MobileNetV2** & **VGG16** \\ \hline Train & 0.914 \(\pm\) 0.072 & 0.962 \(\pm\) 0.075 & 0.815 \(\pm\) 0.145 & 0.970 \(\pm\) 0.013 & 0.955 \(\pm\) 0.012 \\ Test & 0.922 \(\pm\) 0.075 & 0.926 \(\pm\) 0.029 & 0.827 \(\pm\) 0.151 & 0.908 \(\pm\) 0.017 & 0.917 \(\pm\) 0.018 \\ Validation & 0.899 \(\pm\) 0.048 & 0.906 \(\pm\) 0.028 & 0.809 \(\pm\) 0.127 & 0.920 \(\pm\) 0.064 & 0.905 \(\pm\) 0.019 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean and SD of AUC with single modality images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AUC** & **ACC (\%)** & **SENS (\%)** & **SPEC (\%)** & **PPV (\%)** & **PPV (\%)** \\ \hline CNN & Train & 0.914 & 87.1 & 90.2 & 91.4 & 94.5 & 89.7 \\  & Test & 0.922 & 89.6 & 91.4 & 90.5 & 91.2 & 88.7 \\  & Validation & 0.899 & 84.5 & 88.7 & 89.1 & 86.9 & 84.4 \\ \hline DeU-Net & Train & 0.962 & 89.9 & 98.1 & 89.9 & 92.1 & 87.1 \\  & Test & 0.926 & 90.4 & 94.1 & 83.2 & 94.3 & 83.6 \\  & Validation & 0.906 & 89.5 & 92.6 & 81.9 & 97.9 & 87.3 \\ \hline GoogLeNet & Train & 0.852 & 79.6 & 83.6 & 80.1 & 82.4 & 84.7 \\  & Test & 0.871 & 77.5 & 85.4 & 79.7 & 81.6 & 83.5 \\  & Validation & 0.839 & 80.0 & 78.1 & 80.5 & 80.0 & 79.1 \\ \hline InceptionV3 & Train & 0.815 & 78.8 & 80.8 & 90.3 & 80.8 & 79.0 \\  & Test & 0.827 & 79.7 & 81.0 & 89.1 & 81.0 & 73.5 \\  & Validation & 0.809 & 77.3 & 78.7 & 92.1 & 88.1 & 81.2 \\ \hline MobileNetV2 & Train & 0.970 & 95.9 & 98.4 & 96.5 & 98.2 & 94.2 \\  & Test & 0.908 & 88.4 & 94.7 & 90.2 & 94.7 & 82.5 \\  & Validation & 0.920 & 81.0 & 96.3 & 91.9 & 95.4 & 87.1 \\ \hline VGG16 & Train & 0.955 & 86.4 & 95.3 & 97.1 & 95.3 & 94.9 \\  & Test & 0.917 & 87.7 & 91.5 & 93.2 & 91.5 & 86.2 \\  & Validation & 0.905 & 81.1 & 90.1 & 91.0 & 89.7 & 84.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of different neural network in training with single modality images. the other is to predict a false positive (FP). \(NPV\) means negative predict value, which is contrary to PPV. NPV represents how many of the samples predicted to be negative were correct. Test means training cohorts. Validation means validation cohort. Prove means independent prove cohort.