## 5 Discussion  
This work introduces CNN-based architectures for classifying surgical tools in laparoscopic images. Two training strategies were explored which are training with images containing one tool (single-label training) and training with images sampled from complete procedure videos at a rate of 1 Hz. Furthermore, the study demonstrates the benefits of attention modules, feature fusion and temporal modeling for classifying the type of surgical tool in laparoscopic images. The results in Figure 4 show a significant variation in the AP of different tools across the tested architectures. The CNN-SE-FFM-BiLSTM model generally outperformed the other model, particularly for tools like Scissors, Clipper and Irrigator, achieving AP values of 90.73%, 97.62% and 91.70%, respectively. The model's effectiveness can be attributed to its use of multiple SE blocks within the CNN layers, which enhance feature representation. The CNN-FFM-SE-BiLSTM model, while slightly underperforming compared to CNN-SE-FFM-BiLSTM, still showed promising results, especially in detecting frequently used tools like Hook, with AP values of 99.57% (see Figure 4). This model showed lower performance on tools like Scissors and Irrigator (89.12% and 90.46% AP, respectively). The classification performance was improved by introducing multiple SE blocks before fusing mid level to high level features which made the CNN-SE-FFM-BiLSTM model the best performer for the multi-label classification problem over all surgical instruments. The proposed models demonstrate that the inclusion of SE blocks and temporal modeling can significantly enhance the classification accuracy of certain tools, though challenges remain in consistently detecting tools that are less frequently used or more likely to be obscured in the surgical field. Compared to state-of-the-art methods, the proposed CNN-SE-FFM-BiLSTM architecture demonstrated competitive performance. The mean average precision (mAP) for the CNN-SE-FFM-BiLSTM model is 94.63%, which marks a significant improvement over earlier approaches like ToolMod [11], MTRC [14], and Nwoye [25] (see Table 6). These results demonstrate the efficacy of combining spatial and temporal features to classify surgical tools. In comparison to the CNN-LSTM model in [12], our approach shows a comparable performance in mAP, validating the benefit of integrating SE blocks with feature fusion modules. However, the performance on minority classes like Scissors and Irrigator still indicates an area where further improvements are necessary. While the proposed models showed competitive performance, a slight but noticeable difference in performance was observed between the single-label and the multi-label approaches. The CNN-FFM-SE model, when applied in a single-label context, achieved excellent performance, with a mean average precision (mAP) of 95.32%. However, when the model was adapted for multi-label classification, as seen in the CNN-SE-FFM-BiLSTM and CNN-FFM-SE-BiLSTM architectures, the performance dropped, with mAP values of 94.63% and 94.26%, respectively. This drop in performance can be attributed to the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tool** & **ToolMod [11]** & **CNN-SE-MSF-LSTM [10]** & **MTRC [14]** & **Nwoye [25]** & **CNN-SE-FFM-BiLSTM** & **CNN-FFM-SE-BiLSTM** \\ \hline Grasper & 68.71 & 91.00 & 84.7 & 99.7 & 90.65 & 89.77 \\ Bipolar & 92.36 & 97.30 & 90.1 & 95.6 & 95.71 & 95.96 \\ Flock & 93.50 & 99.80 & 95.6 & 99.8 & 99.60 & 99.57 \\ Scissors & 63.07 & 90.30 & 86.7 & 86.9 & 90.73 & 89.12 \\ Clipper & 60.75 & 97.40 & 89.8 & 97.5 & 97.62 & 97.87 \\ Irrigator & 66.91 & 95.60 & 88.2 & 74.7 & 91.70 & 90.46 \\ Specimen Bag & 72.20 & 98.30 & 88.9 & 96.1 & 96.38 & 96.90 \\
**Mean** & 73.93 & 95.60 & 89.1 & 92.9 & 94.63 & 94.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AP per tool comparison between different state-of-the-art approaches and our multi-label approaches. increased complexity of multi-label classification.