Best Faculty Award
Surekha Kamath
Manipal Institute of Technology, India
Surekha Kamath is associated with Manipal Institute of Technology, India. The supplied academic profile identifies Artificial Intelligence as the subject area and reports 55 documents, 772 citations, and an h-index of 11. The listed publications concern artificial intelligence methods for medical image analysis, including liver segmentation, MR image preprocessing for rectal cancer diagnosis, and liver abnormality detection. These topics provide context for reviewing the researcher’s scholarly record. The reported metrics and individual publication contributions should be considered alongside verified bibliographic records, teaching achievements, originality, methodological quality, and documented academic contributions. [1] [2] [3]
Abstract
Surekha Kamath is associated with Manipal Institute of Technology, India, and the subject area of Artificial Intelligence. The supplied academic profile reports 55 documents, 772 citations, and an h-index of 11. The three listed publications address automatic liver segmentation from multiphase computed tomography, preprocessing techniques for rectal cancer diagnosis using magnetic resonance images, and a comparison of machine learning and deep learning methods for detecting liver abnormalities. These topics provide context for research at the intersection of artificial intelligence and medical image analysis. Publication authorship and individual contributions should be confirmed using the linked records. [1] [2] [3]
Keywords
Best Faculty Award; Artificial Intelligence; Machine Learning; Deep Learning; Medical Image Processing; Liver Segmentation; Computed Tomography; Magnetic Resonance Imaging; Rectal Cancer Diagnosis; Computer-Aided Diagnosis; Scopus; Research Impact.
Introduction
Artificial Intelligence includes computational approaches used for tasks such as pattern recognition, classification, prediction, and image analysis. The supplied publications relate to medical imaging, including liver segmentation from CT scans, preprocessing MR images for rectal cancer diagnosis, and detecting liver abnormalities using machine learning and deep learning methods. The supplied profile identifies Surekha Kamath with Manipal Institute of Technology, India, and reports 55 documents, 772 citations, and an h-index of 11. These details provide academic and research context but do not independently establish teaching performance or award eligibility. [1] [2] [3]
Research Profile
The supplied profile identifies Artificial Intelligence as the subject area. The provided publications concern medical image segmentation, image preprocessing, and the application of machine learning and deep learning to diagnostic problems. The first reference focuses on automatic liver segmentation from multiphase CT using a modified SegNet and an atrous spatial pyramid pooling (ASPP) module. The second addresses preprocessing techniques for rectal cancer diagnosis using MR images. The third compares machine learning and deep learning methods for detecting liver abnormalities. The researcher’s authorship and specific contributions should be confirmed through the linked publication records. [1] [2] [3]
Research Contributions
The supplied references represent three themes in medical image analysis. The first concerns automatic liver segmentation from multiphase CT images using a modified SegNet architecture and an ASPP module. The second addresses image preprocessing techniques for rectal cancer diagnosis using magnetic resonance images. The third compares machine learning and deep learning approaches for the detection of liver abnormalities. Together, these topics relate to image preparation, segmentation, pattern recognition, and computer-aided diagnostic analysis. Specific methods, results, and individual contributions should be evaluated using the original publications rather than inferred from their titles alone. [1] [2] [3]
Publications
The three supplied references are: a study on automatic liver segmentation from multiphase CT using a modified SegNet and ASPP module; a study on preprocessing techniques for rectal cancer diagnosis using MR images; and a comparison of machine learning and deep learning methods for detecting liver abnormalities. DOI links are provided in the References section. The original publisher records should be consulted to confirm complete bibliographic details, publication dates, author lists, and the researcher’s contribution to each work. [1] [2] [3]
Research Impact
The supplied profile reports 55 documents, 772 citations, and an h-index of 11. These bibliometric indicators provide a snapshot of recorded scholarly activity and citation visibility. They should not be treated as standalone measures of research quality, teaching effectiveness, or overall academic excellence. A balanced evaluation can also consider originality, methodological rigor, publication quality, reproducibility, student mentorship, and documented academic contributions. Bibliometric values may change as indexing databases are updated. [1] [2] [3]
Award Suitability
For consideration under the Best Faculty Award, the supplied information provides an academic affiliation, a stated subject area, bibliometric indicators, and three publication references related to artificial intelligence and medical image analysis. A complete assessment would also require evidence relevant to the award’s official criteria, such as teaching performance, student mentorship, research originality, academic leadership, institutional service, and documented professional achievements. This profile summary does not imply that the award has been granted or that eligibility has been independently confirmed. [1] [2] [3]
Conclusion
The supplied profile identifies Surekha Kamath with Manipal Institute of Technology, India, and Artificial Intelligence as the subject area. The reported metrics are 55 documents, 772 citations, and an h-index of 11. The three listed publications concern liver segmentation from CT images, preprocessing MR images for rectal cancer diagnosis, and machine learning and deep learning methods for detecting liver abnormalities. These references provide context for the stated research area. A comprehensive Best Faculty Award assessment would also require verified evidence of teaching, mentorship, and broader academic contributions. [1] [2] [3]
External Links
References
- Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module.
https://doi.org/10.1007/s42979-024-02719-2
- Preprocessing Techniques for Rectal Cancer Diagnosis using MR Images.
https://doi.org/10.1145/3651781.3651809
- Comparison of machine learning and deep learning methods for detection of liver abnormality.
https://doi.org/10.1201/9781003363781-4