Papers
2
Total Citations
16
H-Index
2
About
Inayat Khan is a rising researcher whose work bridges the critical intersection of medical imaging, oncology, and advanced deep learning. His primary research areas include lung cancer diagnostics, computer vision for industrial safety, and the application of hierarchical attention models in image analysis. Khan’s most-cited paper, "Current investigative modalities for detecting and staging lung cancers: a comprehensive summary" (2022, 11 citations), provides a vital synthesis of modern diagnostic techniques, offering clinicians and researchers a clear roadmap for improving early detection and staging accuracy. This work underscores his commitment to translating computational methods into tangible clinical impact. More recently, Khan introduced "WallNet: Hierarchical Visual Attention-Based Model for Putty Bulge Terminal Points Detection" (2024, 5 citations), a novel architecture that applies visual attention mechanisms to solve a specialized industrial problem—demonstrating his versatility in adapting AI for real-world applications beyond healthcare. Though early in his career, Khan’s focused contributions to medical imaging and attention-based models signal a promising trajectory. His work not only advances technical frontiers but also emphasizes practical, deployable solutions, making him a researcher to watch in the evolving landscape of AI-driven diagnostics and visual analysis.
Research Focus
Key Achievements
Top Papers
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