Yogesh Kumar Prajapat
Papers
1
Total Citations
5
H-Index
1
About
Yogesh Kumar Prajapat is a researcher at the forefront of surgical robotics and computer vision, whose work focuses on integrating artificial intelligence with medical tool manipulation. His most-cited paper, "Training a Multi-task Model for Classification and Grasp Detection of Surgical Tools Using Transfer Learning" (2023), has garnered 5 citations and represents a significant contribution to the field. In this work, Prajapat developed a multi-task learning framework that simultaneously classifies surgical instruments and predicts optimal grasp points, leveraging transfer learning to overcome the challenge of limited annotated medical data. This innovation directly addresses a critical need in robot-assisted surgery, where precise tool handling is essential for patient safety and procedural efficiency. By enabling a single model to perform both classification and grasp detection, his approach reduces computational overhead and improves real-time performance in operating room settings. Prajapat's research bridges the gap between deep learning and practical surgical applications, demonstrating how AI can enhance the dexterity and autonomy of robotic systems. His work holds promise for advancing minimally invasive procedures and training next-generation surgical robots, marking him as an emerging voice in the intersection of machine learning and healthcare technology.
Research Focus
Key Achievements
Top Papers
- 1