Surendra Kumar Panda

Work place: Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, 752054, India

E-mail: surendrapanda000@gmail.com

Website: https://orcid.org/0009-0003-7470-8124

Research Interests:

Biography

Surendra Kumar Panda is currently pursuing a Ph.D. in Computer Science and Engineering at C.V. Raman Global University. He has published several research papers in national and international journals and conferences, demonstrating his strong dedication to academic research. His primary areas of interest include Artificial Intelligence, Machine Learning, and Medical Image Processing. He focuses on applying intelligent systems and advanced image analysis techniques to solve real-world problems, particularly in healthcare. Through his work, he aims to improve medical diagnosis and treatment by developing better methods for analyzing medical images. He actively collaborates with fellow researchers and is committed to contributing valuable knowledge to both academia and practical technology.

Author Articles
An IoMT enabled Deep Insight of MR Images for Brain Tumor Segmentation with Classification Using an Elevated UNet-RESNet Model

By Surendra Kumar Panda Ram Chandra Barik Ganapati Panda Suvamoy Changder

DOI: https://doi.org/10.5815/ijigsp.2025.04.03, Pub. Date: 8 Aug. 2025

Brain tumors are a prominent cause of mortality on a global scale. The American Brain Tumor Association reports 90,000 primary brain tumor diagnoses annually, highlighting the need for improved diagnostic methods. Delaying brain tumor identification can result in significant financial costs and considerable suffering for patients. Timely identification of brain tumors is crucial for preserving both financial resources and human lives. Physicians’s manual identification of brain tumors is quite challenging. Early and precise brain tumor detection is crucial to addressing these concerns. The incorporation of the Internet of Medical Things (IoMT) coupled with deep learning (DL) is essential for advancing contemporary healthcare solutions. The proposed work presents the IoMT-UNet-ResNet model, an advanced DL method designed specifically for accurately identifying and classifying brain tumors in MR image data. By harnessing the potential of the IoMT, the model effortlessly combines UNet for precise spatial delineation and ResNet-50 for sophisticated feature learning, resulting in outstanding accuracy. This model proves to be an invaluable asset for radiologists, as it simplifies and improves the precision of brain tumor analysis through the use of MRI data. The IoMT enables radiologists to effortlessly access and analyze diagnostic information in real-time, leading to enhanced patient care and results in the field of neuroimaging. The proposed IoMT-UNet-ResNet model outperforms by comparing and validating the existing technique.

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