Twinkle Bansal

Papers

Total Citations

H-Index

About

Twinkle Bansal’s research lies at the intersection of deep learning and medical image analysis, with a particular focus on developing computational tools for disease detection. Her most cited work, a comprehensive review on segmentation techniques for tuberculosis detection using deep learning, critically examines how image segmentation—a cornerstone of medical imaging—can be harnessed to improve diagnostic accuracy. The paper surveys a wide range of computational methods, from scene understanding to augmented reality, but centers on their transformative potential in healthcare, especially for tuberculosis, a global health challenge. While her citation count is still building, this review has already garnered attention for synthesizing complex, interdisciplinary knowledge into a clear framework for researchers and practitioners. Bansal’s contributions are especially notable for bridging the gap between advanced computer vision techniques and real-world medical applications, offering a roadmap for integrating deep learning into clinical workflows. Her work underscores the growing role of artificial intelligence in public health, and as her research portfolio expands, she is poised to make further impacts in automated diagnostic systems.