Yepuganti Karuna

About

Dr. Yepuganti Karuna received B.E and M. Tech degrees from CRR College of Engineering, Eluru, AP and NIT Tiruchirappalli, Tamilnadu in the years 2007, 2009 respectively, and the Ph.D. degree in Multirate Signal Processing from the Vellore Institute of Technology, Vellore, India, in 2019. I started my academic career as an Assistant professor at VIT, Vellore in March 2010 and worked in various academic positions in VIT, Vellore till 31.05.2024. Currently, working as an Associate Professor in the School of Electronics Engineering, VIT-AP University, Amaravati.. I have published a total of 50+ international journal and conference publications. My Interests include Biomedical Signal Processing, Medical Image Processing, Artificial Intelligence, Machine Learning, and Deep Learning.

Employment

• VIT-AP University Associate Professor
  2024 - Present
• Vellore Institute of Technology University Assistant Professor (Senior Grade 2)
  2023 - 2024
• Vellore Institute of Technology University Assistant Professor (Senior)
  2014 - 2022
• Vellore Institute of Technology University Assistant Professor
  2010 - 2014

Education

• Vellore Institute of Technology University PhD
  2014 - 2019
• National Institute of Technology Tiruchirappalli M.Tech
  2007 - 2019

Projects & Funding

Projects & funding information is unavailable.

Publications (29)

• Ground Station-Free UAV Communication Architecture for Secure Connectivity and Autonomous Mission Continuity
  IEEE Access 2026 DOI: 10.1109/ACCESS.2026.3693824
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• An segformer-mix vision transformer-based GIST segmentation in fused CT-PET images
  PLOS One 2026 DOI: 10.1371/journal.pone.0355109
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• An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19
  PLOS ONE 2024 DOI: 10.1371/journal.pone.0309651
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• Retinal vessel segmentation to diagnose diabetic retinopathy using fundus images: A survey
  International Journal of Imaging Systems and Technology 2024 DOI: 10.1002/ima.22945
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• A survey of deep learning for MRI brain tumor segmentation methods: Trends, challenges, and future directions
  Health and Technology 2023 DOI: 10.1007/s12553-023-00737-3
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• Segmentation and Analysis Emphasizing Neonatal MRI Brain Images Using Machine Learning Techniques
  Mathematics 2023 DOI: 10.3390/math11020285
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• Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3317176
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• A Novel Strong Decorrelation Approach for Image Subband Coding Using Polynomial EVD Algorithms
  Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 2021 DOI: 10.1007/s40010-019-00646-z
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• Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images
  International Journal of Imaging Systems and Technology 2020 DOI: 10.1002/ima.22451
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• Magnetic resonance brain images individual recognition with PCA
  International Journal of Innovative Technology and Exploring Engineering 2019
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