Suvamoy Changder

Work place: Department of Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal 713209, India

E-mail: schangder.cse@nitdgp.ac.in

Website: https://orcid.org/0000-0002-4733-2323

Research Interests:

Biography

Suvamoy Changder is an Associate Professor in the Department of Computer Science and Engineering at the National Institute of Technology (NIT) Durgapur. He earned his Ph.D. from NIT Durgapur in 2011 and has been a faculty member there since 2000. His research interests encompass Information Security, Steganography, Watermarking, and Quantum Computing. Dr. Changder has contributed to over 50 publications, including journal articles and conference proceedings. Beyond academia, he serves as the Chief Warden for all boys’ hostels at NIT Durgapur and is a member of the Internal Complaints Committee, reflecting his commitment to student welfare and campus governance. Dr. Changder also contributes to the broader academic community through his involvement in national advisory committees, such as the one for CIACON 2025.

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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