Deep Learning Model for Instrument Detection in Medical Surgeries and Avoiding Mistakes

Abstract

In the fast-evolving healthcare sector, accurate detection and classification of medical tools are essential for enhancing surgical efficiency and patient safety. This paper presents a novel approach to automatic medical device detection using advanced computer vision and deep learning, specifically the YOLOv8 model. The system is trained on a dataset containing various instruments like scalpels, forceps, and scissors, with data preprocessing, augmentation, and transfer learning techniques applied to boost performance despite limited training data. Designed for real-time operation in surgical environments, the model is evaluated using metrics such as accuracy, precision, and recall to ensure reliable performance.