SURGICAL TOOLS

This dataset was created to advance computer vision and object detection systems for surgical tool recognition in clinical settings. The research hypothesis focuses on enhancing the accuracy and reliability of machine learning models to identify surgical tools under diverse, real-world conditions. By compiling a robust image collection that reflects the complexities of surgical environments, the dataset aims to minimize overfitting and boost model performance for practical applications. It consists of 6,000 high-quality images, with 5,000 manually captured at Amrita Vishwa Vidyapeetham, Chennai, India, during January and February 2025, and 1,000 sourced from various online platforms to add variability. The dataset spans nine categories of surgical tools: forceps, hemostats, scalpels, mayo scissors, syringes, bandage scissors, episiotomy scissors, surgical gloves, and medical cotton, offering a broad representation of tools used in surgeries. Notable findings include a significant subset of 1,520 images showcasing both overlapping and non-overlapping tool configurations, mimicking real surgical scenarios. This is vital for training models to manage cluttered or complex arrangements. The dataset also captures tools under diverse conditions—blur, artificial blood, varying backgrounds, lighting from dim to bright, and 360-degree angles—ensuring exposure to challenges encountered in operating rooms.