# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
## 4 Dataset, Experiments and Discussions
### Discussion  
The readily available binary classification approaches are hardly suitable for multiple classes and require additional resources and effort. On the other hand, the extant work on multiple-class classification requires a large-scale dataset. Some approaches such as the transfer learning-based methods, DLMCC-Net [31] and TLEDA-Net [33], can somehow resolve the problems, but the layer adjustment challenges and the dataset for pretext tasks and the dataset for the downstream may result in domain mismatch. The pre-trained models may converge, but they will fall in a local minimum. Therefore, the performance in these cases will not be better than training from scratch. The challenges are more comprehensive than transfer learning when it comes to training the network from scratch. The proposed PMCAD-Net also provides a solution for 4-way classification for detecting and diagnosing various stages of Alzheimer's disease. The comparison to other competing techniques demonstrates that our model is critical in accurately diagnosing AD, EMCI, LMCI and NC in a single model framework. This will aid in determining additional illness severity levels as compared to the binary level of classification in some of the previous methods [33]. The proposed multiclass classification of various stages of Alzheimer's disease outperformed the competitor approaches. Comparison with the state-of-the-art approaches is based on the ADNI, OASIS, and Kaggle datasets. The overall performance is shown in Table 2, where we demonstrate the effectiveness of the system in terms of various evaluation metrics. Our PMCAD-Net achieves a superior numeric score. The performance is further elaborated in the form of box plots, as shown in Figure 4. In this figure, the average analysis and individual analysis are explicitly shown to illustrate the performance of our method. The generalized comparison with other competitor methods, including the CNN-3D model [40], 3D-CNN-PAD [38], NIBR-Net [29], DSMAD-Net [30], DLMCC-Net [31], SCNN [28], and TLEDA-Net [33] is also shown in Table 3. The generalized comparison of the performance is based on F1-score, accuracy, recall (i.e., sensitivity), and positive prediction (i.e., precision) metrics, which are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Authors** & **Methods** & **Modalities** & **Distinction** & **Dataset** & \begin{tabular}{c} **F1** \\ **Measure** \\ \end{tabular} & **Accuracy** & \begin{tabular}{c} **Sensitivity** \\ **Recall** \\ \end{tabular} & \begin{tabular}{c} **Positive** \\ **Precision** \\ \end{tabular} \\ \hline [PERSON] et al. [35] & DEMNET & MRI & \begin{tabular}{c} AD, MCI, \\ LMCI, NC \\ \end{tabular} & Kaggle & 95.2 & 95.2 & 95 & 95.2 \\ \hline [PERSON] et al. [7] & \begin{tabular}{c} CNN-AD \\ (VGG-16) \\ \end{tabular} & MRI & AD, CN, MCI & ADNI & 95 & 95.13 & 96 & 96.3 \\ \hline [PERSON] et al. [34] & ADTL-Net & MRI & \begin{tabular}{c} AD, MCI, \\ LMCI, NC \\ \end{tabular} & Kaggle & 94.7 & 95.7 & 92.3 & 91.9 \\ \hline Proposed Work & \
\begin{tabular}{c} PMCAD-Net \\ (This work) \\ \end{tabular} & MRI &
\begin{tabular}{c} AD, MCI, \\ LMCI, NC \\ \end{tabular} & ADNI & 96.34 & 99.2 & 96.3 & 96.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed work with various state-of-the-art approaches in terms of evaluation metrics.