## 2 State of the Art  
Recent approaches have primarily employed deep learning techniques, with convolutional neural networks (CNNs) serving as a foundation, often combined with other methods  
Figure 1: Examples of challenges in surgical tool detection: (**a**) blood and tissue occlusion, (**b**) motion blur, (**c**) variable lighting, (**d**) smoke and fog obstruction.  
to enhance performance and address specific challenges in the field. [PERSON] et al. introduced a hierarchical neural network architecture that incorporates both spatial and temporal information. Their method combines a CNN with two long short-term memory (LSTM) models to capture temporal relationships across subsequent laparoscopic images. Evaluation on the Cholec80 dataset using Monte Carlo six-fold cross-validation showed a 3.00% improvement in mean average precision (mAP) compared to baseline methods [10]. However, the authors noted that integrating the three models into an end-to-end framework could potentially offer additional discriminative spatiotemporal properties.  
To address the challenge of imbalanced datasets, [PERSON] et al. [11] proposed in another study a CNN-based approach utilizing resampling methods and a loss-sensitive learning strategy. This work highlighted the bias in CNN training caused by imbalanced distribution of the training data. As a result, CNNs struggle with minority classes such as scissors and irrigator in the Chlolec80 data set. The proposed approaches by [PERSON] et al. showed improved performance in categorizing rarely used surgical instruments.  
In another study, data augmentation methods were explored to improve model generalization [12]. Uniform, random, or distinctive backdrop patterns and artificially produced images were used as training patterns. Their experiments showed that classification accuracy increased by 10% when trained with augmented data. Interestingly, training on datasets with identical backgrounds rather than random backgrounds allowed the CNN model to focus more effectively on areas containing target items such as surgical tools. However, the low performance on original background datasets indicated a significant dependence on background patterns and weak generalization capacity, highlighting the ongoing challenges in developing robust models for surgical tool classification.  
Attention mechanisms have also been explored to improve surgical tool detection. [PERSON] et al. [13] proposed a deep learning technique for surgical tool localization in laparoscopic images, which was trained on binary tool presence data. The model uses an attention-based CNN, from which gradient class activation maps (Grad-CAMs) were extracted. The Grad-CAMs were then processed to generate bounding boxes around the surgical instruments. The performance of this method was evaluated using the Dice similarity coefficient. Results showed that the proposed attention-CNN achieved a mean tool localization precision of 72.4%, significantly outperforming the base CNN model, which had a precision of 28.3%. However, the method lacked high accuracy for certain instruments such as irrigators, clippers, and scissors.  
Recent work has also focused on multi-task learning and feature fusion. [PERSON] et al. [14] developed a Multi-Task Recurrent Convolutional Network with Correlation Loss (MTRCNet-CL) to simultaneously improve phase recognition and surgical instrument identification. This approach demonstrated superior performance on the Cholec80 dataset, achieving a mAP of 89.1% for tool presence identification.