# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
## 4 Dataset, Experiments and Discussions
### Experimental Evaluations and Discussions
#### 4.3.5 F1 Measurement  
The F-Metric is a method for combining accuracy and recall into a single measure that encompasses both and is widely utilized in classification tasks. \[F1-Measure=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{8}\]  
The evaluation results for the classification are shown in Table 2. In this table, we provide the results with a 15% dataset of train-test split. We generalized the comparison and selected several state-of-the-art approaches for comparison, including a CNN-3D model [40], 3D-CNN-PAD [38], NIBR-Net [29], DSMAD-Net [30], DLMCC-Net [31], SCNN [28], and TLEDA-Net [33]. The comparison with all of these methods is shown in Table 3. The comparison was based on multiple modalities, where the distinction of each of the approaches is also shown beside the type of dataset. The comparison in this Table is shown in terms of accuracy, where the proposed method outperformed the state-of-the-art methods. Next, we extended the canvas of experiments and included several other methods and metrics to evaluate the proposed approach. The competitor methods include DEMNet [35], CNN-AD (VGG-16) [7], and ADTL-Net [34]. The comparison with these methods was based on the F1-score, accuracy, sensitivity, and positive predictive value. The results are shown in Table 4 with a 20% & 80% test and train split, respectively. The comparison of the MRI modality on various datasets demonstrates the overall recall, precision, and prediction score, where our method outperformed the competitor approaches. We also present the graphical representation for the overall sensitivity, accuracy, specificity, and precision in Figure 4. The plots demonstrate the visual analysis for the four classes (i.e., NC, EMCI, LMCI, and AD.) described as four groups (i.e., Group1-Group4). As shown in Figure 4 group 1, group 2, group 3, and group 4 represent the sensitivity, accuracy, specificity, and precision, respectively, for each of the four classes (i.e., NC, EMCI, LMCI, and AD) along-with the average value for these four parameters and classes. The graphical representation is based on the overall performance of the PMCAD-Net, as shown in Table 2. The behavior of these plots is illustrated explicitly in Table 5, where various features of the graphs illustrate the overall performance of the proposed network. The comparison illustrates that our method outperforms several states of the art approaches. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Classes** & **Sensitivity** & **Accuracy** & **Specificity** & **Precision** & **Overall Average** \\ \hline NC & 99.3 & 98.3 & 99.5 & 99.5 & 99.15 \\ EMCI & 99.4 & 99.6 & 99.1 & 99.2 & 99.32 \\ LMCI & 98.7 & 99.4 & 99.8 & 99.6 & 99.37 \\ AD & 99.5 & 98.3 & 99.7 & 99.5 & 99.25 \\ Average per group (Proposed Work) & 99.2 & 98.9 & 99.5 & 99.4 & 99.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of the performance in terms of overall accuracy of the PMCAD-Net on various classes, including normal controls (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). Figure 4: The graphical analysis of the performance of the proposed network. Box plots are shown for the sensitivity, accuracy, specificity and precision for all the four groups (i.e., Group 1–4); each group is comprised of 4 classes (i.e., NC, EMCI, LMCI, and AD). The average is also shown for all groups (i.e., Group 1–4).