## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.1 Experimental Results and Comparative Analysis of Different Models

Table 4: Performance comparison between various network models. primarily attributed to the EMASlideLoss function, which effectively mitigates sample imbalance in lung organoid datasets. This study visualizes the absolute error weights of each model using bar charts (Figure 6). Among the evaluated models, Orga-Date exhibits a balanced distribution across error types, highlighting its robust performance and generalization capability in lung organoid detection. A comprehensive evaluation of computational cost (FLOPs) and parameter count for each model was conducted using a 640 \(\times\) 640 input resolution. The results are summarized in Table 6.  
Our model (Ours) has 2.25 M params, 6.3 GFLOPs, 246 FPS, and 4.6 MB size. It outperforms YOLOv8n/10n/11n/12n in parameters and size, matches YOLOv11n in GFLOPs, and shows competitive speed. It is far more efficient than RTDETR-r18 and Faster R-CNN. A 3D scatter plot of detection accuracy versus computational efficiency (Figure 7) illustrates the trade-off between performance and resource usage. Given that Faster R \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Params (M)** & **GFLOPs (s)** & **FPS** & **Model Size (MB)** \\ \hline YOLOv8n & 3.0 & 8.1 & 303 \(\pm\) 5 & 6.0 \\ YOLOv10n & 2.7 & 8.2 & 416 \(\pm\) 8 & 5.5 \\ YOLOv11n & 2.58 & 6.3 & 280 \(\pm\) 6 & 5.2 \\ YOLOv12n & 2.5 & 5.8 & 231 \(\pm\) 4 & 5.2 \\ RTDETR-r18 & 19.9 & 56.9 & 100 \(\pm\) 3 & 38.6 \\ Faster R-CNN & 28.28 & 149.6 & 69 \(\pm\) 3 & 108 \\ Ours & 2.25 & 6.3 & 246 \(\pm\) 5 & 4.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the number of computations and parameters for each model. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Cls** & **Loc** & **Both** & **Dupe** & **Bkg** & **Miss** \\ \hline YOLOv8n & 6.33 \(\pm\) 0.44 & 0.70 \(\pm\) 0.18 & 0.41 \(\pm\) 0.12 & 0.00 \(\pm\) 0.00 & 11.06 \(\pm\) 0.74 & 0.00 \(\pm\) 0.00 \\ YOLOv10n & 6.78 \(\pm\) 0.47 & 0.60 \(\pm\) 0.17 & 0.53 \(\pm\) 0.14 & 0.01 \(\pm\) 0.01 & 12.03 \(\pm\) 0.78 & 0.00 \(\pm\) 0.00 \\ YOLOv11n & 6.60 \(\pm\) 0.45 & 0.63 \(\pm\) 0.18 & 0.47 \(\pm\) 0.10 & 0.01 \(\pm\) 0.00 & 12.31 \(\pm\) 0.81 & 0.00 \(\pm\) 0.00 \\ YOLOv12n & 7.11 \(\pm\) 0.49 & 0.68 \(\pm\) 0.20 & 0.41 \(\pm\) 0.13 & 0.00 \(\pm\) 0.00 & 11.14 \(\pm\) 0.76 & 0.00 \(\pm\) 0.00 \\ RTDETR-r18 & 13.66 \(\pm\) 0.75 & 0.57 \(\pm\) 0.18 & 0.46 \(\pm\) 0.14 & 0.01 \(\pm\) 0.01 & 8.61 \(\pm\) 0.66 & 0.00 \(\pm\) 0.00 \\ Faster R-CNN & 32.88 \(\pm\) 1.36 & 2.19 \(\pm\) 0.37 & 1.73 \(\pm\) 0.30 & 2.29 \(\pm\) 0.37 & 2.84 \(\pm\) 0.41 & 0.20 \(\pm\) 0.12 \\ Ours & 6.23 \(\pm\) 0.44 & 0.44 \(\pm\) 0.15 & 0.32 \(\pm\) 0.11 & 0.00 \(\pm\) 0.00 & 10.61 \(\pm\) 0.72 & 0.00 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the absolute weight of each model error. Figure 6: Comparison of absolute error weights across models. CNN's computational cost (149.6 GFLOPs) is 9.8\(\times\) higher than the average of the other models (15.3 GFLOPs)--with no corresponding gain in accuracy--it is excluded from this plot to prevent skewed analysis due to scale imbalance. The figure demonstrates that the proposed Orga-Date model not only boosts detection performance effectively but also lowers computational requirements and parameter counts, thereby outperforming the other models compared. To assess real-world detection capability, four high-performing models--YOLOv8n, YOLOv11n, RTDETR-r18, and Orga-Date--were tested on representative lung organoid images. As shown in Figure 8, Orga-Date consistently delivers the highest detection accuracy across all test cases. Its superiority is especially pronounced in low-contrast scenes and Lumen regions with subtle deformations, where it demonstrates markedly enhanced fine-grained feature extraction compared to the alternatives.