Interpretable Deep Learning for Surgical Tool Management

This paper presents a novel convolutional neural network framework for multi-level classification of surgical tools. Our classifications are obtained from multiple levels the model, and high accuracy is by adjusting depth layers selected predictions. enhances interpretability overall predictions providing comprehensive set each tool. allows users to make rational decisions about whether trust model based on pieces information, can be evaluated against other consistency error-checking. The prediction achieves promising results surgery tool dataset knowledge base, which important contributions our work. provides viable solution intelligent management tools in hospital, potentially leading significant cost savings increased efficiencies.