Kaliappan. M | Medical Image Processing | Best Researcher Award

Published on by Cryogenicist

Best Researcher Award

Kaliappan. M — Ramco Institute of Technology, India

Kaliappan. M
Affiliation Ramco Institute of Technology
Country India
Scopus ID 55315684100
Documents 76
Citations 1,767
h-index 25
Subject Area Medical Image Processing
Event Cryogenicist Global Awards
ORCID 0000-0001-6177-7428

Kaliappan. M is a researcher and his record identifies research activity is on spanning artificial intelligence, data science, wireless networks, and medical-image-related applications. The profile also records peer-reviewed publications and continuing scholarly activity.[1]

Abstract

This academic recognition profile presents the documented research record of Kaliappan. M and his research record identifies his research including deep learning, Big Data analytics, and wireless ad-hoc networks. His documented scholarly activities include medical image processing, machine learning, big data analytics, and related computational applications. [3]

Keywords

Kaliappan M, Medical Image Processing, Deep Learning, Big Data Analytics, Artificial Intelligence, Wireless Ad-Hoc Networks, Machine Learning, Medical Imaging, Researcher Award, Cryogenicist Global Awards.

Introduction

The research profile combines computer science methodologies with application-oriented studies. The ORCID record documents a Professor and Head role in Artificial Intelligence and Data Science at Ramco Institute of Technology and identifies a continuing publication history across computing and interdisciplinary research.[1]

Research Profile

The documented research interests include deep learning, Big Data analytics, and wireless ad-hoc networks. More recent recorded work includes multimodal medical image classification using a unified vision transformer, demonstrating an application of contemporary machine-learning methods to biomedical image analysis.[1]

Research Contributions

Kaliappan. M has contributed to research in deep learning, Big Data analytics, and medical image processing, with applications across intelligent computing and biomedical systems. His documented work includes machine-learning approaches for medical image classification and disease prediction. The research record also covers secure routing, energy optimization, and data-driven computational models.

Publications

The ORCID record lists 27 works and includes publications in journals such as Biomedical Signal Processing and Control, Neural Computing and Applications, Journal of Medical Systems, and Cluster Computing. Examples include a 2026 article on multimodal medical image classification and earlier studies involving machine learning, data analytics, and network systems.[2]

Research Impact

The research profile records 76 documents, 1,767 citations, and an h-index of 25. These metrics are presented as profile information supplied for this recognition page and should be interpreted according to the indexing database and date on which the metrics are retrieved.[3]

Award Suitability

The documented publication activity, interdisciplinary research themes, and bibliometric indicators provide a factual basis for presenting Kaliappan. M for consideration under the Best Researcher Award. The stated subject area for this profile is Medical Image Processing, while the wider record includes several related computing disciplines.

Conclusion

Kaliappan. M’s profile reflects sustained academic activity across artificial intelligence, data analytics, medical image processing, and networking research. His recorded publications and his research metrics establish a structured scholarly profile for documentation within the Cryogenicist Global Awards recognition framework. [3]

References

  1. ORCID. (2026). Kaliappan M, ORCID record 0000-0001-6177-7428. ORCID.
    https://orcid.org/0000-0001-6177-7428
  2. Elsevier. (n.d.). Scopus author details: Kaliappan M, Author ID 55315684100. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=55315684100
  3. Kaliappan, M., Mariappan, E., Manimaran, V., & Revathi, B. (2026). Unified vision transformer for multimodal medical image classification. Biomedical Signal Processing and Control.
    https://doi.org/10.1016/j.bspc.2026.110431
  4. Vimal, S., Kalaivani, L., Kaliappan, M., Suresh, A., Gao, X.-Z., & Varatharajan, R. (2020). Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks. Neural Computing and Applications.
    https://doi.org/10.1007/s00521-018-3788-3
  5. Geetha, R., Sivasubramanian, S., Kaliappan, M., Vimal, S., & Annamalai, S. (2019). Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier. Journal of Medical Systems.  .
    https://doi.org/10.1007/s10916-019-1402-6

Vanessa Murad | Medical Imaging | Best Researcher Award

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Dr. Vanessa Murad | Medical Imaging | Best Researcher Award

Assistant Professor | University Health Network | Canada

Dr. Vanessa Murad is a clinical researcher at the University Health Network in Toronto, Canada, recognized for her growing contributions to evidence-based clinical science and multidisciplinary healthcare research. With a portfolio of 33 publications and 68 citations, she has developed an emerging yet impactful scholarly presence supported by extensive collaboration with more than 70 co-authors across diverse clinical domains. Dr. Murad’s work reflects a commitment to advancing diagnostic precision, improving therapeutic approaches, and refining models of patient care through methodologically sound and clinically relevant investigations. Her h-index of 5 highlights steady scholarly influence and the increasing visibility of her contributions within the research community. Operating within one of Canada’s leading academic health science centers, she actively bridges clinical practice and research, supporting the translation of new insights into improved patient outcomes. Dr. Murad’s collaborative research engagements underscore her integration into global networks focused on innovation, clinical excellence, and interdisciplinary problem-solving. As her publication record continues to expand, she remains dedicated to generating knowledge that enhances clinical decision-making, informs healthcare policy, and addresses the evolving challenges faced by healthcare systems worldwide.

Profiles: Scopus | Google Scholar

Featured Pubilcations

1. Varela Londoño, S. P., Chinchilla Salcedo, T., & Murad Gutiérrez, V. (2015). Prácticas de crianza en niños y niñas menores de seis años en Colombia. Zona Próxima, 193–215.
Cited by: 138

2. Murad, V., Kohan, A., Ortega, C., Prica, A., Veit-Haibach, P., & Metser, U. (2024). Role of FDG PET/CT in patients with lymphoma treated with chimeric antigen receptor T-cell therapy: Current concepts. American Journal of Roentgenology, 222(2), e2330301.
Cited by: 19

3. Mirshahvalad, S. A., Mesci, A., Murad, V., Kohan, A., Ortega, C., Veit-Haibach, P., … (2023). [18F]-FDG PET in anal canal cancer: A systematic review and meta-analysis. European Journal of Nuclear Medicine and Molecular Imaging, 51(1), 258–277.
Cited by: 15

4. Murad, V., Barragán, C., & Rivera, H. (2018). Ultrasound evaluation of the parathyroid glands. Revista Colombiana de Radiología, 29(1), 4861–4866.
Cited by: 14

5. Gutiérrez, V. M., & Enciso, J. A. R. (2018). Liver elastography: What it is, how it is done, and how it is interpreted. Radiología (English Edition), 60(3), 183–189.
Cited by: 12

Dr. Vanessa Murad advances precision oncology and molecular imaging through clinically driven research that enhances diagnostic accuracy and patient outcomes. Her work bridges scientific discovery with translational practice, strengthening evidence-based care for complex malignancies.

Yihua Zhu | Radiology | Best Researcher Award

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Dr. Yihua Zhu | Radiology | Best Researcher Award

Specialist, University of California San Francisco, United States

A dedicated research specialist at the Bone Quality Research Lab within the Department of Radiology and Biomedical Imaging at UCSF, this individual focuses on the innovative application of advanced image processing and analysis techniques. Their expertise lies particularly in high-resolution peripheral quantitative computed tomography (HR-pQCT), dual-energy X-ray absorptiometry (DXA), and magnetic resonance imaging (MRI) for the comprehensive assessment of bone quality.

Profile

Orcid

Education 🎓

This individual earned a PhD in Oral and Craniofacial Sciences from the University of California San Francisco in June 2024, following a Master of Science in Medical Physics from the University of Pennsylvania obtained in December 2018. Their foundational education includes a Bachelor of Arts degree in Physics from the University of Southern California, completed in June 2016.

Experience 🧑‍🔬

Currently serving as a Specialist in Radiology at UCSF, this researcher contributes significantly to the Bone Quality Research Lab. Prior to this role, their doctoral research at UCSF involved pioneering work in optical imaging applications within dentistry. This included the development of a novel dual-wavelength short-wave infrared (SWIR) intraoral camera for the early detection of caries, a technology featured in several NIH-funded clinical studies. Furthermore, their experience extends to the utilization of optical coherence tomography (OCT) for monitoring the remineralization process in dental lesions and the application of machine learning methodologies to enhance the precision of fluorescence-guided intraoperative therapy.

Research Interests 🦴🦷

The primary research interests of this specialist revolve around the application of advanced imaging modalities for the assessment of bone quality and the early detection and monitoring of dental caries. This includes the development and implementation of innovative optical imaging techniques, such as short-wave infrared (SWIR) imaging and optical coherence tomography (OCT), and the utilization of sophisticated image processing and machine learning algorithms to extract meaningful clinical information from these imaging data. Their research bridges the fields of radiology, biomedical imaging, and dentistry, with a focus on translating advanced technologies into practical diagnostic and therapeutic tools.

Publications Top Note 📚

Optimal Wavelengths for Multispectral Short Wavelength Infrared Transillumination and Reflectance Imaging for Caries Detection. Diagnostics. 2025 Apr 18. Fried D, Zhu Y. This article likely investigates the most effective wavelengths within the short-wave infrared spectrum for detecting dental caries using both transillumination and reflectance imaging techniques.

In vitro Assessment of lesion activity using simultaneous time-resolved reflectance imaging at 1300 and 1950 nm. Lasers Med Sci. 2024 Aug 27;39(1):223. Wycoff S, Zhu Y, Fried D. PMID: 39191998. This study investigated the use of time-resolved reflectance imaging at two specific wavelengths to assess the activity of dental lesions in vitro.

Clinical SWIR and CP-OCT imaging of interproximal lesions. BMC Oral Health. 2024 Aug 17;24(1):959. Zhu Y, Le O, Xue J, Wycoff S, Fried D. PMID: 39153971; PMCID: PMC11330057. This research explored the clinical application of short-wave infrared (SWIR) and cross-polarization optical coherence tomography (CP-OCT) for imaging lesions located between teeth.

Short-Wavelength Infrared Imaging of Infected and Affected Dentin. Diagnostics (Basel). 2024 Mar 30;14(7). Ng M, Ho YC, Wycoff S, Zhu Y, Fried D. PMID: 38611657; PMCID: PMC11012186. This publication focused on the use of short-wavelength infrared imaging to differentiate between infected and affected dentin, a crucial aspect in caries management. Cited by: 1.

Diagnostic Performance of Multispectral SWIR Transillumination and Reflectance Imaging for Caries Detection. Diagnostics (Basel). 2023 Aug 31;13(17). Zhu Y, Ng C, Le O, Ho YC, Fried D. PMID: 37685362; PMCID: PMC10487234. This study evaluated the effectiveness of multispectral short-wave infrared transillumination and reflectance imaging techniques for the detection of dental caries. Cited by: 2.

Monitoring lesion activity on primary teeth with CP-OCT and SWIR reflectance imaging. Lasers Surg Med. 2023 Aug;55(6):601-609. Zhu Y, Kim J, Lin B, Fried D. PMID: 37172310. This study explored the use of cross-polarization optical coherence tomography (CP-OCT) and short-wave infrared (SWIR) reflectance imaging for monitoring the activity of lesions on primary teeth. Cited by: 1.

Measurement of the Depth of Lesions on Proximal Surfaces with SWIR Multispectral Transillumination and Reflectance Imaging. Diagnostics (Basel). 2022 Feb 26;12(3). Zhu Y, Fried D. PMID: 35328150; PMCID: PMC8947190. This research focused on using short-wave infrared multispectral transillumination and reflectance imaging to measure the depth of lesions on the surfaces between teeth. Cited by: 3.

Conclusion 🎯

This research specialist has demonstrated a strong commitment to advancing the field of biomedical imaging, particularly in the areas of bone quality assessment and dental caries detection. Their innovative work in developing and applying advanced optical imaging techniques, coupled with their expertise in image processing and analysis, holds significant potential for improving diagnostic capabilities and ultimately enhancing patient care in both radiology and dentistry.