# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 1 Introduction  
Alzheimer's disease (AD) is the most common cause of progressive dementia in older adults [1]. AD can present as mental disorder, memory loss, language problem, or unpredictable behaviors. It occurs due to the death of neurons in different parts of the brain, and then throughout all of the areas of the brain at the final stage of AD. Brain tissue shrinks significantly. This disease generally occurs in older patients at an average age of 65 years and varies from individual to individual [2]. AD is not yet curable. Disease severity can increase for ten years after the diagnosis. The causes and reasons for the disease are still unknown to the medical community. Current treatment methods help manage symptoms in patients with AD. However, no treatment is available to completely cure the disease even though several medicines have been approved and tested recently. Worldwide, more than 44 million people suffer from AD. This number will increase to more than 76 million by 2030 [3]. To diagnose [PERSON]'s disease in its early stages, proper medical images need to be studied. Positron emission tomography (PET) magnetic resonance imaging (MRI), structural magnetic resonance imaging (SMRI), functional MRI (fMRI), and diffusion tensor imagingcan indicate the biomarkers for human neuroimaging data [4; 5; 6; 7]. MRI is fully non-invasive and is available worldwide. The mini-mental state examination (MMSE) is conducted by physicians to determine the impairment of patients with [PERSON]'s disease [8]. However, as part of automatic detection, image-based analysis is needed to correctly classify the stages of AD. MRI images clearly show the soft tissues of the brain. The temporal and parietal lobes of the brain can be seen clearly visualized on MRI. Changes in these lobes result in cognitive impairments in humans. The physician's diagnosis is fully based on visual observation of the MRI. Interpretation of the MRI may vary from person to person. Therefore, an automatic diagnosis system is needed to assist physicians in making correct decisions about the disease. Most studies on Alzheimer's disease have used MRI images for analysis. In these four proposed methods, MRI images were used for Alzheimer's disease analysis. Figure 1 shows MRI images of a healthy person and a person suffering from dementia. Several classification methods for AD have recently been reported. [PERSON] et al. and [PERSON] et al. reviewed Alzheimer's disease classification based on MRI data [9; 10]. Joint regression and classification [11] and weighted multi-modality-based classification [12] are mainly used to classify the disease. Two sets of strategies were applied in the brain morphometric analysis. Voxel-based morphometry and deformation-based morphometry are the two approaches currently used by the research community. In addition, machine-learning methods are used for the classification of Alzheimer's disease [13]. Support vector machine (SVM)-based Alzheimer's disease classification is mainly used by researchers. In this method, the SVM extracts high-dimensional features from MRI data and builds classification models to classify the disease. However, it mainly relies on the manual outlining of brain structures [14]. Lattice-independent component analysis and dendritic computing classifiers are used to perform MRI image classification of Alzheimer's patients and normal patients [15]. Binary classifiers and single-neuron lattice models are used to perform the classification [16]. Initially, the disease-related features in the brain images are extracted by voxel morphometry analysis, and then a manifold-based semi-supervised learning framework is used to classify the disease [17]. Gray-level histogram-based MRI classification are also performed to identify anatomical changes in the hippocampus and thalamus regions [18]. Recently, deep-learning-based methods have been developed in the areas of computer vision, image understanding, natural language processing, etc. Deep-learning methods have also been used in medical image analyses.