# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 1 Introduction
Prior feature selection is not required, and the input data can be optimally inferred [19]. This is one of the significant differences between deep-learning-based methods and other state-of-the-art machine learning methods. In addition, visual saliency-based methods have recently been used for the analysis and classification of [PERSON]'s disease. Visual saliency maps play a vital role in the fields of computer vision and cognitive science. Automatic image analysis methods were inspired by researchers because the visual perception of radiologists was utilized by the saliency map to extract relevant disease regions [20]. Many algorithms and methods have  
Figure 1: MRI images of (**a**) the brain of a healthy person (**b**) the brain of a person suffering from dementia. been developed for visual saliency detection. Important and unimportant regions are segregated to perform image compression [21], segmentation [22], etc. By incorporating visual saliency analysis, the overall performance of the system is high with respect to performance metrics [23]. Many neurodegenerative diseases have very challenging image patterns that are not captured by region of interest (ROI) calculations and are time-consuming. The discrimination between mild and severe AD is challenging in the automatic diagnosis process. In general, AD analysis is carried out with respect to the two datasets, ADNI and OASIS. Many literature reviews have been conducted on MRI image analysis for both datasets. The proposed method used the OASIS dataset for the experimental investigation. [PERSON]'s disease classification methods depend on personal clinical and demographic data. Four categories were used to analyze the AD classification using the proposed method. The proposed method highlights the importance of saliency maps in AD analysis. Initially, the input MRI images were preprocessed using a statistical parametric mapping tool. Multiscale decomposition was performed using a wavelet transform. Wavelet decomposition was performed to obtain the essential features for saliency-map generation. Bottom-up and top-down saliency maps were obtained to obtain the final saliency map. Bottom-up saliency depends on the image features of the MRI. It is computed using the edge, texture, and orientation characteristics of the MRI images. An elliptical local binary pattern descriptor was leveraged to find low-level orientation characteristics. Top-down saliency uses the domain knowledge of the input. Simple multiple-kernel learning (MKL) and simple and efficient MKL(SEMKL) were utilized to classify Alzheimer's disease and normal patients. The experimental results showed reliable results in the performance metrics of accuracy, sensitivity, specificity, and f-measure. The results were compared with eight state-of-the-art methods that used the OASIS dataset for experimental analysis. The novelty of the proposed method lies in the use of an elliptical local binary pattern descriptor in the bottom-up saliency and usage of MKL and SEMKL for Alzheimer's disease classification. Section 2 presents the proposed methodology, and this subsection presents the bottom-up and top-down saliency maps in detail. Section 3 presents the experimental results, performance metrics, and comparisons. Section 4 presents a discussion of the proposed method. The final section concludes the paper.