# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 5 Diagnosis Results and Analysis
### Convolution Neural Network Diagnostic Results
#### 5.1.2 Fusion Image Training Model  
According to the objective evaluation of the fused images, the best algorithm is the frequency weighted wavelet fusion algorithm. Therefore, the fused image set was selected to train different models. In order to compare different performance between models in diagnosing Alzheimer's disease, multiple deep learning models were implemented. Mean and Sd of AUC shown are in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **CNN** & **GoogLeNet** & **DeU-Net** & **InceptionV3** & **MobileNetV2** & **VGG16** \\ \hline Train & 0.935 \(\pm\) 0.042 & 0.916 \(\pm\) 0.053 & 0.967 \(\pm\) 0.022 & 0.891 \(\pm\) 0.038 & 0.945 \(\pm\) 0.050 & 0.949 \(\pm\) 0.040 \\ Test & 0.927 \(\pm\) 0.046 & 0.909 \(\pm\) 0.017 & 0.929 \(\pm\) 0.037 & 0.922 \(\pm\) 0.046 & 0.909 \(\pm\) 0.065 & 0.928 \(\pm\) 0.010 \\ Validation & 0.916 \(\pm\) 0.053 & 0.897 \(\pm\) 0.013 & 0.941 \(\pm\) 0.017 & 0.849 \(\pm\) 0.043 & 0.899 \(\pm\) 0.063 & 0.856 \(\pm\) 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean and SD of AUC with fusion images.  
Figure 16: Confusion matrix of DeU-Net with single modality images.  
Detailed training results were listed in Table 7.  
In Table 7, AUC means area under the receiver operating characteristic curve. ACC means accuracy. SENS means sensitivity. SPEC means specificity. PPV means positive predict value. NPV means negative predict value. Test means training cohorts. Validation means validation cohort. Prove means independent prove cohort. We also recorded the training loss curve of DeU-Net, where orange represents the training set and blue represents the test set. It can be seen from Figure 17 that the training loss decreases steadily, which shows the good performance in training.  
In Figure 18, we also provide the confusion matrix in training with fusion images.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **AUC** & **ACC (\%)** & **SENS (\%)** & **SPEC (\%)** & **PPV (\%)** & **NPV (\%)** \\ \hline CNN & Train & 0.935 & 89.4 & 93.1 & 95.2 & 88.2 & 88.2 \\  & Test & 0.927 & 91.5 & 92.5 & 93.4 & 91.0 & 93.3 \\  & Validation & 0.916 & 90.9 & 90.9 & 90.9 & 89.5 & 89.5 \\ \hline DeU-Net & Train & 0.967 & 95.7 & 97.9 & 94.8 & 90.2 & 88.2 \\  & Test & 0.929 & 91.6 & 95.1 & 87.1 & 95.1 & 85.6 \\  & Validation & 0.941 & 90.5 & 92.4 & 84.0 & 99.7 & 84.9 \\ \hline GoogLeNet & Train & 0.916 & 91.1 & 88.5 & 86.4 & 84.9 & 77.3 \\  & Test & 0.909 & 88.4 & 86.1 & 82.3 & 87.6 & 71.5 \\  & Validation & 0.897 & 86.0 & 79.2 & 81.7 & 77.5 & 82.1 \\ \hline InceptionV3 & Train & 0.891 & 85.1 & 78.4 & 95.1 & 85.3 & 85.2 \\  & Test & 0.922 & 89.3 & 85.0 & 90.7 & 82.0 & 71.5 \\  & Validation & 0.849 & 81.2 & 82.3 & 94.0 & 89.0 & 91.2 \\ \hline MobileNetV4 & Train & 0.945 & 91.4 & 94.3 & 96.0 & 96.9 & 93.0 \\  & Test & 0.909 & 89.7 & 92.5 & 90.2 & 97.2 & 86.7 \\  & Validation & 0.899 & 88.1 & 99.2 & 88.9 & 94.9 & 82.0 \\ \hline VGG16 & Train & 0.949 & 89.9 & 94.2 & 97.2 & 98.2 & 92.9 \\  & Test & 0.928 & 88.5 & 92.5 & 93.1 & 97.3 & 89.6 \\  & Validation & 0.856 & 84.2 & 83.9 & 87.0 & 84.9 & 83.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of different neural network in training with fusion images.  
Figure 17: Training loss with fusion images.