## 1 Introduction  
The evolution of surgery has been remarkable, stretching from rudimentary practices to the advanced techniques we see today [1]. The development of Future Operating Rooms (FORs) increasingly relies on Context-Aware Systems (CASs), which streamline surgical workflows and enhance patient outcomes through real-time data analysis [2]. CASintegration in FORs will enhance knowledge-based systems with data-driven surgical treatments. These data-driven treatments necessitate collaborative interactions for multi-perspective knowledge sharing among medical teams, such as surgical and anesthetic teams [3,4]. In this context, CASs must be capable of envisioning workflows within the OR and understanding the current situation by fusing data from various perspectives, including both procedural and patient-related data [5].  
The emerging field of Surgical Data Science (SDS) leverages big data analysis to model surgical processes and develop CASs, representing a significant leap forward in the advancement of surgical procedures [6]. The automatic identification of surgical tools is a crucial component for modelling the surgical processes. Thus, the recognition of surgical tools is addressed in this work. However, Minimally Invasive Surgery (MIS) presents unique challenges, particularly due to the narrow operative space and limited field of view [7,8]. These constraints make precise navigation of surgical tools critical. Moreover, this problem involves other challenging aspects such as rapid camera movements, variable tissues, and obstructive elements like smoke and blood, as illustrated in Figure 1. These challenges hinder the performance of existing methodologies. Combining spatial and temporal features can overcome the limitations of previous approaches and provide more accurate and robust tool classification in MIS environments.  
In this work, we propose a novel architecture to identify surgical tools in laparoscopic images. Our approach employs a modified DenseNet121 architecture [9], to learn spatial features, integrated with a Bidirectional LSTM (BiLSTM) network to capture temporal dependencies in laparoscopic videos. We developed two deep learning multi-label models for surgical tools. The first model incorporates multiple Squeeze-and-Excitation (SE) attention modules, a Feature Fusion Module (FFM) and a BiLSTM layer. The second model employs a single SE block after the FFM, followed by a BiLSTM layer. Additionally, we explored a simpler and computationally less-expensive single-label surgical tool classification model for classifying surgical tools in cholecystectomy images, where only one tool is present at a time.  
The primary objectives of this study are the following:  
1. To develop a novel architecture that effectively combines spatial and temporal feature extraction for surgical tool classification.
2. To investigate the effect of integrating a single SE block, multiple SE attention modules and combining features at different levels on classification performance.
3. To explore the classification performance of the proposed approaches on multi-label and single-label data.
4. To compare our method's performance against state-of-the-art approaches in surgical tool classification.