# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 4 Discussion  
Computer-aided detection has attracted significant attention for brain image analysis. This is possible with advancements in machine learning and computational intelligence techniques. The proposed method deals with visual saliency-based Alzheimer's disease \begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy (A)** & **Sensitivity (S)** & **Specificity (SP)** & **F-Measure (Fm)** \\ \hline Division 1 & 91.18 & 88.21 & 89.34 & 90.91 \\ Division 2 & 88.26 & 86.15 & 87.33 & 87.81 \\ Division 3 & 87.54 & 85.78 & 86.13 & 86.91 \\ Division 4 & 84.23 & 82.91 & 83.21 & 84.12 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of the proposed approach. analysis. This was accomplished using a saliency analysis of MRI images. Bottom-up and top-down information streams achieve the precise detection of AD and normal patients. Bottom-up saliency highlights the regions that are associated with AD diagnosis. This was obtained from different multiscale features. The major focus is on the construction of bottom-up saliency maps. An elliptical local binary pattern descriptor (ELBPD) was used to analyze the texture features of the MRI images. Ellipse-like topologies help to obtain feature information from different orientations. To distinguish potential objects, a circular neighborhood was added to the texture descriptor. The top-down saliency map uses the domain knowledge of the MRI brain images. It adaptively chooses meaningful information. The entire saliency strategy allows the identification of structural regions that can be quantitatively related to AD detection. Information from the VBM is usually used for the statistical identification of different categories. The SPM8 tool was used to pre-process the MRI images. The pre-processed image will help to obtain a correct classification and generate more accurate results. The obtained saliency maps consisted of information for detecting AD and normal patients. None of the parts of the saliency map did not contain relevant information for detecting AD. To analyze AD well, the feature space size was reduced using the Fisher discriminant ratio. The MKL and SEMKL methods were adopted to discriminate between AD classes. MKL does not suffer from overfitting. The final decision was based on the weighted average of the SVM models. The kernel weights in MKL which are the most prominent in the classifier, were used to identify the data sources well. The proposed study was conducted using the Open Access Series of Imaging Studies (OASIS) dataset. The present investigation involved extensive validation and parameter studies. Different factors are involved in bottom-up and top-down saliency, which are assessed based on the classification accuracy. This allows us to check the influence of different visual features and image scales on the final detection between AD and normal classes. The effective version of the proposed method attained an equivalent performance to that of state-of-the-art comparison methods in the Table 3. The comparison between the [PERSON] et al. [16] method and the proposed method showed an average increment of 9.4% in accuracy and other performance metric calculations. [PERSON] et al. [16] used dendritic computing to implement binary classifiers. Single-neuron lattice models were used to compute classification. With respect to performance metrics, the proposed method outperformed the methods of [PERSON] et al. [40] and [PERSON] et al. [39]. The primary reason was the inclusion of elliptical local binary descriptors in the saliency map computations. The results of the proposed method were compared with eight state-of-the-art methods and produced 89.12% classification accuracy. The proposed approach identifies the most relevant information for AD detection using saliency maps. These maps were derived from the orientation features, specifically at 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\) and at different scales.