## 3 Experiments
### Discussion  
AD is the foremost source of dementia in elderly people accounting for 50-60% of all the cases. Correct classification of pre-clinical AD, MCI due to AD, and dementia due to AD remain challenging issues for the researchers. Among the existing literature, much variability has been noticed in the context of sensitivity and specificity [44]. The reported sensitivity has ranged between 41 and 100% with a median of 87% while the reported specificity has ranged between 37 and 100% with a median of 58% [45,46]. Since most of the studies have reported only sensitivity, a general assumption has arisen that the clinical diagnosis is highly accurate. In the current study, we tested the consistency of the proposed method on the following four indices: (1) sensitivity, (2) specificity, (3) accuracy, (4) F1 score, and (5) AUC. Moreover, for a better understanding of the obtained result, we also plotted the ROC curve. Various studies have computed features based on ROI, VBM, and statistical approaches [13, 45, 46]. These feature extraction techniques are highly sensitive to geometric deformation and/or perturbation of MRI images under consideration. In recent years, orthogonal moments are proven to be excellent feature descriptors in the field of medical imaging [8, 13, 38, 45, 46]. These orthogonal moments have the advantage of needing lower precision to represent the difference of the same accuracy as the monomials. Another advantage of the orthogonal moments is that they are rotationally invariant naturally. Therefore, any unexpected perturbation will not affect the measurement confronting thus the inherent uncertainty. In the first experiment, we test and compare the proposed system for AD vs. MCI. We also plot the ROC curve, as shown in Figure 2a. To understand the effect of orthogonality on feature extraction, we compare RM (non-orthogonal) texture-based features with PHTs (orthogonal) texture-based features to diagnose AD vs. MCI in T-1 MRI images. The classification of these features is done by the proposed SaDE-WNN classifier and for comparative analysis of the proposed classifier. We also classify these features with other similar classifiers available in the literature (DEWNN and Ada-DEWNN). The proposed classifier with PCT texture features obtained a high value of indices (93.7% accuracy, 86.0% sensitivity, 98.0% specificity) followed by PCET, ZM, and RM. It shows more uniform and precise results compared to DEWNN and Ada-DEWNN. The classification results using RM textural features show the lowest results for all cases. A higher value of sensitivity specifies that there are few false-negative results, and thus fewer cases of the disease are missed. On the other hand, high specificity means that there are few false positive results [14,27]. In the current research, false positives are less important than false negatives because the false-negative answer may lead the patient into a serious condition. In the second experiment, the proposed method shows similar behavior as in the case of MCI vs. HC depicted in Table 2. The classification results based on the non-orthogonal (RM) feature sets show the lowest performance among all other methods. The classification results for PCT with Sade-DEWNN shows higher sensitivity (92.9 %), and AUC (0.98, Figure 2c) compared to ZM, PCET, and RM. The ZM texture-based features show almost the same sensitivity and accuracy as PCT. However, this difference is not statistically significant (\(p>0.05\)). It could also be noted that the proposed PCT shows a slight improvement in AUC compared to ZM and PCET (Figure 2). In contrast, in our results for MCI vs. HC and AD vs. MCI, we obtained improved results for classification of AD vs. HC for all four indices (sensitivity, specificity, accuracy, and AUC) for all feature sets. The best accuracy, sensitivity, specificity, and AUC of PCT features with SaDE-WNN classifier are 94.4%, 88.7%, 98.9%, and 0.99 respectively (Table 2 and Figure 2b).