Using Convolutional Neural Networks for Edge Detection in Medical Images to Determine Surgery Instrument Tools

Abstract

Edge detection plays a crucial role in medical image analysis, particularly in surgical settings where accurate identification of surgical instrument tools is essential. In this paper, we explore the use of Convolutional Neural Networks (CNNs) for edge detection in medical images to determine surgical instrument tools. We present a comprehensive study that includes dataset selection, preprocessing techniques, network architecture design, training procedures, evaluation metrics, and experimental results. The CNN models were trained on a diverse dataset of medical images with annotated ground truth edge maps. The models demonstrated superior performance compared to traditional edge detection algorithms and handcrafted feature-based approaches, achieving high accuracy and robustness in capturing surgical instrument boundaries. We evaluated the models using metrics such as Intersection over Union (IoU), Precision, Recall, F1-Score, and Mean Average Precision (mAP) on a separate test set. this study demonstrates the potential of CNNs for edge detection in medical images to determine surgical instrument tools. The achieved accuracy, robustness, and computational efficiency of the trained models validate their utility in assisting surgeons during surgical interventions.