## 1 Introduction
In a multi-class classification problem, the mean average precision (MAP) for multiple testing should be sufficient to determine the \"goodness\" of the classifier for the objects to be classified. Some only report the average MAP for all the classes, but reporting the MAP for each class would be more helpful in understanding the performance of the classifier. Previous studies using hardware-based solutions, such as RFID, tend to be more stable, but it requires additional financial investment as well as possibly increases the size of the instruments, making them less user-friendly. Machine learning features such as attention-HOG can be selected by the designer and offer more explainability of the grouping results, but they tend to require more computationally intensive pre-processing for each image or video frame, and they are less likely to be able to achieve near real-time performance during classification. The deep-learning architectures seem to be a possible solution to achieve real-time classification, since the feature to be extracted can be computed in real time. However, the detection and classification of the objects can be a bottleneck, especially if the objects to be classified in different classes may exhibit highly similar features. Several modified deep-learning architectures have been proposed in the literature, as discussed above. However, it is not the aim of this paper to propose a new architecture nor is it to compare with the results of previous publications, since the datasets they used are not publically available now. The aim of this paper is to investigate a few well-known, non-modified, deep learning architectures, and based on their MAP performances in classifying the surgical instruments used in a local hospital, choose it as the base model for modification in continuing the investigation in the future. Previous studies presented their results in MAP (mean average precision) of all the surgical instruments as a whole without reporting on the accuracy of each instrument, which is not useful for understanding their performances on each individual type of instrument and the instrument or instruments most likely to be misidentified. So, the purpose of this paper is to investigate the recognition performances on surgical instruments without building a new system but by choosing a few readily available learning architectures that have been studied in other applications without modification to their architecture. The reason for this is that the designs of deep learning architectures are flexible and can be modified to achieve better performances, so it would be important to identify a good base architecture for the purpose of detection and identification of medical instruments, and then extend and modify this base architecture in the future in order to achieve better performances. The purpose of this study is a preliminary investigation into which base deep learning architecture is most suited for classifying surgical instruments. In order to achieve this objective, this study selects three basic deep learning architectures, experimentally finds their accuracies on detecting and identifying individual surgical instruments in our dataset, and compares their performances. The dataset used in this study was generated by extracting frames from real surgery video clips provided by the neurosurgery department of a local hospital in Taiwan. Based on the results of the study presented in [8], the Faster R-CNN (Region-based Convolutional Neural Networks) classifier is chosen as one of the base architectures to study. In addition, the Mask R-CNN [11] classifier is also chosen, since it was claimed that the ROI (region-of-interest)-pooling operation used by the Faster R-CNN network is less accurate and may adversely affect its classification accuracy. So, the Mask R-CNN network proposed a replacement stage, the ROI-alignment stage, in order to get a better accuracy in bounding boxes around the object to be classified.