## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.1 Experimental Results and Comparative Analysis of Different Models  
We analyze model performance across four dimensions: detection accuracy, error decomposition, computational complexity, and practical detection effectiveness. Detection accuracy is measured using metrics such as [EMAIL_ADDRESS], APsmall and APmedium. Since the lung organoid dataset lacks large objects (area \(>\) 96 \(\times\) 96 pixels), APlarge is consistently \(-\)1 and excluded from the analysis to preserve evaluative rigor. Error weight analysis identifies dominant error sources, while computational metrics (e.g., parameter count, model size, and frames per second [FPS]) are evaluated to balance accuracy with deployment efficiency. Finally, visual comparisons of detection outcomes illustrate model-specific strengths and limitations in organoid detection. This study employed 10-fold cross-validation, retaining 1 fold as the validation set per iteration. The performance evaluation system was constructed based on the mean of ten validation results, with the model achieving an average MSE \(<\) 0.001. To evaluate the effects of data augmentation, we trained both YOLOv11n and Orga-Date models using the original dataset and the augmented dataset, respectively. Experimental results demonstrated that data augmentation significantly enhanced detection accuracy. The results are summarized in Table 4.  
Our model achieves the best performance across metrics. With data augmentation, it reaches 81.4% [EMAIL_ADDRESS], surpassing all others. Its Lumen (76.4%) and No Lumen (86.5%) accuracies lead, showing strong class-specific detection. Notably, it excels in small/medium organoids: 51.6% APssmall and 63.5% APmedium, far above Faster R-CNN (26.1%, 45.8%) and outperforming YOLO variants. Even without augmentation, it outperforms most models, highlighting its inherent effectiveness. This confirms its superiority in both overall and category-specific detection. To assess model-specific error patterns, TIDE-based error analysis is conducted, with the results summarized in Table 5.  
It is observed that background false positive errors are prevalent in the experimental model, primarily due to defocused organoid background noise within the dataset. Faster R-CNN demonstrates notably poor performance in the classification error category, aligning with its overall low detection accuracy. In contrast, Orga-Date records the lowest total error weight across categories, confirming its effectiveness in improving detection precision and minimizing cumulative errors. Its strong performance in reducing classification errors is \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Lumen** & **No Lumen** & **[EMAIL_ADDRESS]** & **APsmall** & **APmedium** & **APall** \\ \hline YOLOv8n & 74.0\(\pm\) 0.2\% & 82.4 \(\pm\) 0.3\% & 78.2 \(\pm\) 0.2\% & 46.1 \(\pm\) 0.1\% & 60.3 \(\pm\) 0.2\% & 59.4 \(\pm\) 0.2\% \\ YOLOv10n & 70.6 \(\pm\) 0.2\% & 84.2 \(\pm\) 0.1\% & 77.3 \(\pm\) 0.2\% & 48.2 \(\pm\) 0.2\% & 55.7 \(\pm\) 0.3\% & 54.6 \(\pm\) 0.2\% \\ (without data augmentation) & 65.8 \(\pm\) 0.1\% & 82.8 \(\pm\) 0.2\% & 74.3 \(\pm\) 0.1\% & 40.1 \(\pm\) 0.2\% & 55.8 \(\pm\) 0.1\% & 54.2 \(\pm\) 0.2\% \\ YOLOv11n & 70.8 \(\pm\) 0.2\% & 84.9 \(\pm\) 0.2\% & 77.9 \(\pm\) 0.1\% & 45.1 \(\pm\) 0.2\% & 58.3 \(\pm\) 0.1\% & 58.1 \(\pm\) 0.2\% \\ YOLOv12n & 70.1 \(\pm\) 0.1\% & 83.7 \(\pm\) 0.1\% & 76.9 \(\pm\) 0.1\% & 41.6 \(\pm\) 0.2\% & 54.4 \(\pm\) 0.1\% & 53.6 \(\pm\) 0.1\% \\ RTDETR-r18 & 70.8 \(\pm\) 0.1\% & 84.1 \(\pm\) 0.2\% & 77.4 \(\pm\) 0.1\% & 48.1 \(\pm\) 0.1\% & 59.5 \(\pm\) 0.1\% & 57.4 \(\pm\) 0.1\% \\ Faster R-CNN & 69.4 \(\pm\) 0.2\% & 72.6 \(\pm\) 0.1\% & 71.0 \(\pm\) 0.1\% & 26.1 \(\pm\) 0.2\% & 45.8 \(\pm\) 0.3\% & 44.1 \(\pm\) 0.3\% \\ Ours & 73.4 \(\pm\) 0.1\% & 83.5 \(\pm\) 0.3\% & 78.5 \(\pm\) 0.2\% & 49.8 \(\pm\) 0.1\% & 60.5 \(\pm\) 0.1\% & 59.6 \(\pm\) 0.1\% \\ Ours & 76.4 \(\pm\) 0.1\% & 86.5 \(\pm\) 0.2\% & 81.4 \(\pm\) 0.2\% & 51.6 \(\pm\) 0.1\% & 63.5 \(\pm\) 0.2\% & 62.8 \(\pm\) 0.1\% \\ \hline \hline \end{tabular}
\end{table}