# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 4 Results  
To assess how the model's performance varies with resolution, two distinct input dimensions were evaluated, 320x320 and 640x640. As shown in Table 2, models using a higher resolution achieved better detection accuracy. The YOLOv8l (640) model achieved the highest mAP@50, reaching 0.979. Regarding inference speed, YOLOv8n (320) achieved the fastest performance, with a frame rate of 129.79 FPS. In the class-wise performance analysis, the YOLOv8l (640) and SSD MobileNet V2 FPNLite (640) models, which achieved the highest mAP@50 scores within their respective architectures, were compared. It should be noted that this comparison selection was based solely on mAP@50 and does not imply these are the best models overall, as the optimal choice may vary depending on specific use-case requirements. The comparison, shown in Table 3, reports precision, recall, and AP@50 metrics for each class. The classes include the digits 0-9, as well as medical acronyms such as \"sys\", \"dia\", \"m\", \"p\", and \"spO2\". The YOLOv8l (640) demonstrated superior performance across all classes, with particularly high metrics for class 4, achieving a precision of 0.995 and a recall of 0.975. While the SSD MobileNet V2 FPNLite (640) model exhibited lower precision and recall values, it performed well in class 4 with 0.902 precision and 0.933 \begin{table}
\begin{tabular}{l c c c c c} \hline
**Model** & **Resolution** & **Precision** & **Recall** & **mAP@50** & **FPS** \\ \hline YOLOv3u & 320 & 0.946 & 0.820 & 0.910 & 30.05 \\  & 640 & 0.971 & 0.915 & 0.971 & 9.00 \\ \hline YOLOv5 mu & 320 & 0.922 & 0.822 & 0.902 & 83.68 \\  & 640 & 0.961 & 0.926 & 0.971 & 27.38 \\ \hline YOLOv8n & 320 & 0.879 & 0.675 & 0.786 & **129.79** \\  & 640 & 0.939 & 0.859 & 0.935 & 54.83 \\ \hline YOLOv8m & 320 & 0.925 & 0.833 & 0.908 & 77.28 \\  & 640 & 0.957 & **0.931** & 0.972 & 24.34 \\ \hline YOLOv8l & 320 & 0.950 & 0.842 & 0.923 & 43.25 \\  & 640 & **0.971** & 0.928 & **0.979** & 13.20 \\ \hline SSD & 320 & 0.669 & 0.631 & 0.638 & 16.53 \\  & 640 & 0.812 & 0.835 & 0.838 & 7.84 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of YOLO models and SSD MobileNet V2 FPNLite (320 and 640 input resolutions). recall. Overall, the YOLOv8l (640) model demonstrated higher class-wise performance when compared to the SSD MobileNet V2 FPNLite (640). Table 4 presents a comparative analysis of classification metrics between the YOLOv8l (640) standalone model and the YOLOv8l (640) combined with a CNN classifier. It is essential to emphasize that the classification metrics were derived exclusively from matched bounding boxes, where correspondence between predicted and ground truth boxes was established using an Intersection over Union threshold of 0.5. Only these matched boxes were included in the metrics calculation, while unmatched predictions and ground truths were excluded from the evaluation. This approach focused on the classification performance of the boxes that were successfully matched rather than the overall object detection performance. For the standalone YOLOv8l (640) model, an accuracy of 0.98 was achieved, with high precision, recall, and F1-scores across most classes.