Abdul Wahid

Dongguk University, National University of Sciences and Technology

Papers

Total Citations

H-Index

About

Abdul Wahid is a researcher at the forefront of artificial intelligence and medical image analysis, with a particular focus on enhancing surgical precision through deep learning. His most cited work, "CFFR-Net: A Channel-wise Features Fusion and Recalibration Network for Surgical Instruments Segmentation" (2023, 9 citations), introduces a novel architecture that significantly improves the segmentation of surgical instruments in robot-assisted procedures. By enabling more accurate localization and orientation detection, this contribution directly supports safer and more effective surgical planning. Wahid’s broader expertise in AI is captured in his influential review, "Artificial Intelligence: Evolution, Benefits, and Challenges" (2021, 8 citations), which synthesizes the transformative potential and ethical complexities of the field. His work bridges cutting-edge computational methods with real-world clinical needs, demonstrating a commitment to translating AI advancements into tangible healthcare improvements. With a growing citation record, Wahid is establishing himself as a key voice in the intersection of deep learning and surgical technology, offering valuable insights for both researchers and practitioners seeking to harness AI for critical medical applications.