# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 5 Discussion  
The evaluation of YOLO and SSD models in this study provided key insights into their strengths and weaknesses in detecting and recognizing digits and acronyms on medical device displays. Overall, the results demonstrate that the YOLO models, particularly YOLOv8l (640), exhibited superior performance in terms of mAP@50, precision, and recall when compared to the SSD MobileNet V2 FPNLite models. YOLOv8l (640) achieved the highest mAP@50 of 0.979, and strong class-wise performance for both digits and medical acronyms. The SSD (640) model performed relatively well for certain classes (e.g., class 4 with 0.902 precision and 0.933 recall), but it showed poorer performance compared to the YOLO models for other classes. Figure 7: Sample results from the YOLOv8l (640) standalone model. Analysis of the experimental results demonstrated a fundamental trade-off between detection accuracy and computational efficiency. While higher resolution (640 \(\times\) 640) achieved the highest detection accuracy, it also came at the expense of higher inference time. For instance, YOLOv8n (320) offered the fastest performance at 129.79 FPS, making it a suitable option for real-time applications, but this was achieved at the cost of a lower mAP (0.786). Conversely, YOLOv8l (640), which achieved the highest mAP (0.979), had a much lower inference speed of 13.20 FPS, making it less suitable for real-time use cases where inference time is critical. This highlights the need to balance inference speed and detection performance depending on the application requirements. For real-time patient-monitoring systems where inference speed is crucial, a lighter model such as YOLOv8n may be more appropriate. However, in situations where detection accuracy is the priority, YOLOv8l (640) might be more suitable. To the extent of our knowledge, similar works did not report inference speed, computational efficiency, or frames per second (FPS), focusing solely on standard evaluation metrics for object detection models. The comparison between the standalone YOLOv8l (640) model and the YOLOv8l with the CNN classifier provided additional insight into the classification component performance. The standalone model achieved a higher performance with an accuracy of 0.98. However, upon integrating the CNN classifier, a slight reduction in accuracy to 0.96 was observed, with some classes, such as class 5 and class 2, showing a noticeable drop in all metrics evaluated. For the other classes, the performance remained high or even improved, suggesting that integrating the CNN for classification may be beneficial in certain cases. Nevertheless, further optimization is required to avoid performance loss in specific classes. The analysis of the confusion matrices for both configurations (Figure 5) reinforces these findings. The YOLOv8l standalone model demonstrated fewer misclassifications across almost all classes. In contrast, the combined YOLOv8l-CNN approach demonstrated increased confusion between classes, particularly for the visually similar digits '2' and '5'. This may be due to the classifier's sensitivity to subtle variations in seven-segment digit representations, as these digits can appear visually similar depending on display orientation. The precision-recall (PR) curve for the YOLOv8l (640) model, shown in Figure 6, demonstrates a consistently strong performance across all classes. The curves cluster tightly in the upper-right quadrant, reflecting that the model maintains high precision and recall under different threshold levels. The global mAP@50 across all classes reaches 0.981, confirming the model's performance consistency and generalization capacity. In addition to quantitative metrics, a qualitative assessment was conducted to visually inspect some of the predictions made by the YOLOv8l (640) model. As shown in Figure 7, the model accurately detects digits and acronyms across various medical devices and conditions. It handles differences in lighting, reflections, fonts, and orientations, demonstrating strong generalization.