Nidhi Gupta

OverviewAI-generated summary

Nidhi Gupta's research focuses on the application of machine learning and deep learning techniques to medical image analysis, particularly for the detection and characterization of brain tumors and other medical conditions. Her work has involved developing and evaluating algorithms for tasks such as tumor segmentation, feature extraction, and classification using various imaging modalities like MRI.

Her publications demonstrate a consistent engagement with developing non-invasive and adaptive computer-aided diagnosis (CAD) systems. This includes research on utilizing customized thresholding methods, prominent features, and supervised learning for brain tumor detection from T2-weighted MRIs. She has also explored ensemble learning approaches for glioma detection on brain MRIs by integrating texture and morphological features. More recently, her work has extended to weapon detection in images, employing classical machine learning and deep learning methods, including robust detection in challenging dark environments using specialized deep learning models like Yolov7-DarkVision. Her research also encompasses image enhancement techniques for dark environments.

Gupta's scholarly contributions are reflected in her h-index of 13 and a total of 607 citations across her 78 publications. She has a background in computer science, holding MTech and PhD degrees in the field, and has experience as a postdoctoral fellow at the National Laboratory of Pattern Recognition, Chinese Academy of Sciences. Her technical expertise includes MATLAB, Python, SQL databases, and C programming.

Metrics

• h-index: 13
• Publications: 78
• Citations: 607