Neeru Jindal
Papers
2
Total Citations
6
H-Index
2
About
Dr. Neeru Jindal is a rising researcher at the intersection of machine learning, e-health, and medical image analysis. Her work focuses on developing computational tools to address critical healthcare challenges, particularly through deep learning and image segmentation. In her highly cited 2020 paper, "An OpenSim guided tour in machine learning for e-health applications" (4 citations), she explores the integration of OpenSim—a biomechanical simulation platform—with machine learning to advance e-health technologies, offering a practical framework for researchers entering this interdisciplinary field. More recently, her 2023 review, "Segmentation Techniques for Detection of Tuberculosis Using Deep Learning" (2 citations), provides a comprehensive analysis of image segmentation methods for TB detection, highlighting the role of deep learning in enhancing diagnostic accuracy from medical scans. This work underscores her commitment to applying AI to pressing global health issues. Dr. Jindal’s contributions are particularly valuable for students and researchers seeking to understand how computational methods can be translated into real-world medical applications, bridging the gap between simulation, machine learning, and clinical practice.
Research Focus
Key Achievements
Top Papers
- 1An OpenSim guided tour in machine learning for e-health applications4 citations · 2020
- 2