# Attention Score Enhancement Model Through Pairwise Image Comparison
## 2 Materials and Methods  
### Data and Preprocessing  
In this study, we utilized the dataset from [2], which consists of 3108 clock-drawing images collected between 2019 and 2021 at King Chulalongkorn Memorial Hospital in Bangkok, Thailand. The participants ranged in age from 29 to 90, with a female-to-male ratio of 3:1. The participants were instructed to draw a clock showing the time 11:10 as part of the MoCA assessment. The MoCA results were scanned, anonymized, and cropped to focus solely on the clock drawing using custom programming language (Python 3.8.18). All collected images were resized to 224 \(\times\) 224 pixels, and image augmentation techniques such as brightness adjustment, contrast adjustment, and resizing were applied to ensure the diversity of the image data. However, transformations that could affect the clock evaluation, such as rotation and flipping, were excluded.  
Ultimately, the original 3108 images were augmented 23 times, resulting in a total of 71,484 images. Figure 1 illustrates the various image transformations generated through these augmentation techniques. The augmented dataset significantly increased the number of images to include a wide range of visual conditions, allowing the model to learn various transformations. Specifically, techniques such as brightness adjustment, contrast adjustment, and resizing were used to simulate different visual conditions that may occur in real-world scenarios. For example, brightness and contrast adjustments reflected changes in lighting conditions in the places where participants drew their clocks, while resizing allowed the model to learn the variability in clock sizes. However, rotation and flipping transformations were excluded as they could distort the directionality of the clock, potentially causing confusion during model training.  
Figure 1: Examples of various image augmentations in CDT data: original, brightness adjustment, contrast adjustment, and resizing.  
We used this dataset of 71,484 images to evaluate the impact of dataset size on model performance. When only the original 3108 images were used, the model struggled to fully learn the various patterns within the clock images, leading to a decline in classification performance. By incrementally increasing the dataset size and measuring performance changes, we found that model performance significantly improved once the dataset reached approximately 71,484 images, after which further increases in dataset size yielded little to no performance gains. This suggests that the model was able to generalize effectively and learn from a wide range of visual conditions with a sufficiently large dataset.  
The lack of further improvement beyond 71,484 images indicates that the model had been sufficiently exposed to diverse visual transformations, reaching a saturation point in learning. This finding provides meaningful insight into determining the optimal dataset size for CDT image analysis, which can be used as a guideline for building effective datasets in similar studies. It demonstrates that data augmentation and proper adjustment of dataset size play critical roles in maximizing model performance.