# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 3 Results  
### Dataset  
The experiment was conducted using the Open Access Series of Imaging Studies (OASIS) dataset. The OASIS database consists of brain MRI images [35; 36; 37]. These image data were collected from MRI scans, diagnostic tests, and demographic data. Cross-sectional MRI and longitudinal MRI data are available in the OASIS dataset. MRI images were from 416 subjects between 18 and 96 years of age. The subjects were of both genders, and all were right-handed. A 1.5 T vision scanner was used to capture images from each subject. The MRI image acquisition details included the orientation of the sagittal plane and a flip angle of 10\({}^{\circ}\). For this method, we randomly selected 200 subjects with complete demographic, clinical, or derived anatomic volume information [18]. One hundred patients were diagnosed with AD, and the other half were healthy subjects. The entropy-basedsorting mechanism was used to obtain the most informative 32 images from the axial plane. Hence, 6400 images were used for training, of which 3200 images were AD and the other 3200 images were healthy. Figure 5 shows sample images from the OASIS dataset with AD and normal patient images. The red circles highlight the variations in AD images. Table 1 provides additional information about the subjects.  
All images were analyzed and diagnosed as AD and normal control images. The socio-economic status ranged from 1 (highest) to 5 (lowest). The Mini-Mental State Examination and Clinical Dementia Rating (CDR) were the medical parameters used to examine the images. MMSE scores ranged from 0 (worst) to 30 (best). All brain images consisted of 176 slices. Every single-slice MRI was represented by 176 \(\times\) 208 pixels. Five categories of stages were taken by considering age group, clinical dementia rating (CDR), gender (F/M), and severity of disease. The CDR is a dementia staging factor that provides ratings to each subject.  
Division 1: Age 60-80, CDR=1, 7/6, 86 images, 20 D, 66 N;  
Division 2: Age 60-96, CDR=1, 7/6, 126 images, 28 D, 98 N;  
Division 3: Age 60-80, CDR= 0.5, 21/30, 136 images, 70 D, 66 N;  
Division 4: Age 90-96, CDR= {2,1,0.5}, 198 images, 100 D, 98 N.  
where D is the Alzheimer's disease image and N is the normal patient image. If the CDR is 0.5, the disease is very mild. If CDR is 1, then [PERSON]'s disease is mild and if CDR is 2, then [PERSON]'s disease is moderate.  
AD classification performance depends on clinical and demographic data with respect to patients [33]. It is very difficult to discriminate between patients with very mild AD and those with normal conditions. Four categories were used to analyze the classification of AD. Figures 6 and 7 also show such difficulties by viewing two types--Alzheimer's disease patients and normal patients. In structural images, differentiating between the two types is difficult. The proposed method-based saliency maps exhibit slight variations, which can help in classification tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Numbers**} & \multirow{2}{*}{**Gender**} & \multirow{2}{*}{**Socioeconomic Status**} & \multicolumn{3}{c}{**Age**} & \multicolumn{3}{c}{**MMSE**} & \multicolumn{3}{c}{**CDR**} \\ \cline{4-11}  & & & & **Range** & **Mean** & **Range** & **Mean** & **0** & **0.5** & **1** & **2** \\ \hline AD & 100 & M/F & 2.94 & 66–96 & 78.08 & 15–30 & 24 & 0 & 31 & 17 & 1 \\ Normal & 100 & M/F & 2.88 & 65–94 & 77.77 & 26–30 & 28.96 & 49 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject demographics and dementia status.  
Figure 5: Example brain images of normal and AD subjects.