## 3 Methodology
### Data Preprocessing  
Preprocessing is a significant step in the data mining process. The expression \"trash in, trash out\" is especially applicable to information mining and AI projects. Data gathering strategies are regularly approximately controlled, with out-of-range esteems, impossible information mixes, missing qualities, etc. A preliminary processing of information is set up for primary preparation or for additional examination. Data preprocessing is a cycle of setting up raw information and making it appropriate for an AI model. It is the first and pivotal advance in making an AI model robust. Data cleaning and normalization are techniques used to eliminate anomalies and normalize the information. It takes a structure that can be handily used to make a model. Normalization is an information-based design technique that decreases data redundancy and eliminates unwanted qualities such as insertion, deletion, and update anomalies. The described normalization rules separate larger tables into smaller tables and connect those utilizing connections. Mathematically, the normalization equation is represented as given in Equation (1):  
\[x_{norm} = \frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Total Images** & **Size** & **Classes/** & **Image Format** \\ \hline NIH Chest X-ray & \multirow{2}{*}{2800} & \multirow{2}{*}{1024 \(\times\) 1024} & \multirow{2}{*}{14/200} & \multirow{2}{*}{PNG} \\ Dataset & & & & \\ \hline COVID-19 Chest & \multirow{2}{*}{200} & \multirow{2}{*}{1024 \(\times\) 1024} & \multirow{2}{*}{1/200} & \multirow{2}{*}{PNG} \\ X-ray Image Dataset & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NIH chest X-ray and COVID-19 chest X-ray image dataset. where \(x\) is the input variable as the individual input, \(x_{min}\) and \(x_{max}\) are the minimum and maximum values from that particular feature, and \(x_{norm}\) is the output of the processed input value. However, the images have pixels that are taken as intensity values. The normalization in these images is somehow different in the context of numeric operations. For this purpose, different interpolation methods have been used in matrix normalization. The data become loss-free when we increase or decrease the input image dimensions. The bilinear interpolation method has been used to preprocess the images. In image processing tasks, the neighborhood pixels are utilized in most cases to obtain focused pixel results. Similarly, in the bilinear method, the 2 \(\times\) 2 neighborhood operation is utilized to obtain the weighted average. The horizontal and vertical interpolations are performed using corner points of the given input image as points of reference for further operations. Assume the 4 corner points as \(C_{(1,1)}\), \(C_{(2,1)}\), \(C_{(1,2)}\), and \(C_{(2,2)}\) as given in Equations (2)-(5). \[C_{(1,1)}=\ \frac{(\alpha_{2}-\alpha)}{(\alpha_{2}-\alpha_{1})}\ \frac{(\beta_{2}-\beta)}{(\beta_{2}-\beta_{1})}Q_{1} \tag{2}\]  
The vertical corner points \(C_{(1,1)}\) and \(C_{(2,1)}\) are shown in Equation (2) and (3), where \(\alpha\) is the middle point regarding the x-axis between \(\alpha_{1}\) and \(\alpha_{2}\). The middle point is considered as the 2 \(\times\) 2 neighborhood, where \(\beta\) is taken as the y-axis point, and \(\beta_{1}\), \(\beta_{2}\) are the corresponding 2 \(\times\) 2 neighbors of the middle y point of the considered interpolated point. Q1 and Q2 are the quadrants of the four points of the image. \[C_{(2,1)}=\ \frac{(\alpha-\alpha_{1})}{(\alpha_{2}-\alpha_{1})}\frac{(\beta_{2 }-\beta)}{(\beta_{2}-\beta_{1})}Q_{2} \tag{3}\]  
Equations (4) and (5) are the corner points of the bottom of a given digital lattice. These points are shown in Figure 2.  
The four corner points with their corresponding 2 \(\times\) 2 neighbors for the final point calculations have been shown for a better understanding and interpretation of the given equations of the bilinear interpolation method.