## 2 Materials and Methods
### Offline Base Data Augmentations
#### 2.5.1 Positive/Supportive Data Augmentations  
Positive augmentations, a subset of offline base data augmentations, involve modifications that supportively enhance the dataset. These include variations of the original images  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Augmentation Method** & **Description** & **Training Configuration** \\ \hline Image Rotation & Implements a counter-clockwise rotation on images & Allows a rotation of up to 40 degrees \\ Width Shifting & Moves the image laterally along the x-axis & Allows a shift of up to 45 pixels along the x-axis \\ Height Shifting & Moves the image vertically along the y-axis & Allows a shift up to 45 pixels along the y-axis \\ Shearing & Distorts the image along the width or height axis & Implements a maximum shear angle of 0.2 degrees \\ Zooming & Modifies the image scale, zooming in or out & Enables a maximum of 20\% zoom \\ Horizontal Flipping & Creates a mirror image along the vertical axis & Implements flipping only along the horizontal axis \\ \hline \hline \end{tabular}
\end{table}
Table 1: Online (affine) augmentation approaches, occurring randomly during training.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Base Augmentation** & **Type** & **Description** \\ \hline Baseline & Positive & Original image without any changes \\ Baseline Rotated & Positive & Original image rotated counter-clockwise \\ ROI [39] & Positive & Image segmented to highlight the Region of Interest \\ Horizontal Split 20\% & Positive & Image horizontally split in the middle with a 20\% overlap \\ Horizontal Split 20\% & Positive & Image horizontally split in the middle with a 20\% overlap, and the second part flipped \\ Flip & Positive & Image horizontally split in the middle with a 5\% overlap \\ Horizontal Split & Positive & Image horizontally split in the middle with a 5\% overlap \\ Horizontal Split Flip & Positive & Image horizontally split in the middle with a 5\% overlap, and the second part flipped \\ ROI Split [31] & Positive & ROI segmented image split in the middle with a 20\% overlap \\ ROI Split Flip & Positive & ROI segmented image split in the middle with a 20\% overlap, and the second \\   Noise 05 & Negative & 5\% Gaussian pixel noise added to the baseline \\ Noise 10 & Negative & 10\% Gaussian pixel noise added to the baseline \\ Noise 20 & Negative & 20\% Gaussian pixel noise added to the baseline \\ Noise 50 & Negative & 50\% Gaussian pixel noise added to the baseline \\ Cube 2 & Negative & Image divided into equidistant parts in a 2 \(\times\) 2 grid \\ Cube 3 [40] & Negative & Image divided into equidistant parts in a 3 \(\times\) 3 grid \\ Cube 6 & Negative & Image divided into equidistant parts in a 6 \(\times\) 6 grid \\ No ROI & Negative & Concatenated upper and lower non-ROI parts of the image \\ No ROI Split & Negative & Concatenated upper and lower non-ROI parts of the image, split in the center \\ \hline \hline \end{tabular}
\end{table}
Table 2: Offline-additive base data augmentation sets, citations indicate the first known KOA instance of the approach.  
and mild transformations that maintain the data's core characteristics. Such augmentations are vital for enriching the dataset with beneficial variability, aiding in more nuanced feature extraction and understanding of data variations, ultimately leading to improved model accuracy and reliability.