# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 3 Materials and Methods
### Data Preprocessing  
This study's research steps began with preparing the data used, separating the data into training and test data, followed by the data preparation stage, also known as data pre-processing, modeling using the ResNet-50, ResNet-101, and ResNet-152 architectures, and evaluating the selected model. Some preparation was required before the data entered the modeling process so that the existing data could construct an accurate model and the model could deliver good results. As a result, some transformations or adjustments to the pixel data were required, both to adapt to the model's architecture and to improve the model's accuracy.  
Since the data received had varying pixel sizes, all data sizes were converted to 224 \(\times\) 224 for equalizing the size before being named according to the class or folder that was formed. To speed up model training and uniform values in the data, 0-255-pixel values in the data were normalized by conversion to a scale of 0-1. After normalizing the data, we used data augmentation to diversify the available data, allowing the model the study of additional data throughout the training process, resulting in better training on the targeted characteristics [2].  
The enhancement strengthened the final model's resistance to various types of noise [36]. Shifting, picture reversal, rotation, and image amplification are common augmentation techniques [37]. We changed the image or vertically and horizontally shifted it. Additional versions of the model were formed by adjusting the position of objects in the image, making the model more general. Datasets that were enhanced with an image shift of 10% produced better assessment results than datasets that were not enhanced [38]. As a result, a 10% image shift is used in this investigation.  
Flipping is a technique in image augmentation that involves flipping an image along one of its axes, typically a horizontal or vertical axis, to increase the amount of training data and improve a model's performance in pattern recognition or other image processing tasks. Modifications like these can make the trained model more robust and capable of recognizing objects in different orientations. Datasets with horizontally flipped photos can significantly improve dataset diversity, allowing the training of model with more images from the same dataset and achievement of an evaluation score of more than 90% [39]. As a result, horizontal image inversion is used in this investigation. Meanwhile, rotation in image augmentation is the technique of rotating an image at a specific angle around its center point. The goal is to generate variations in perspective from the same image so that models trained with augmented data become more resistant to changes in the orientation of the same object in the image. Using rotated images in the \(-30^{\circ}\) to \(+30^{\circ}\) range [40] results in a higher level of accuracy. As a result, an image rotation of \(30^{\circ}\) is used in this investigation. Zooming or magnification of an image is also added by zooming in by 20% to improve the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Dataset Samples** \\ \hline Pneumonia & \\ Normal & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._model's assessment value [41]. In Table 2, visualization example of image augmentation are shown.