International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 1229KB)
An IoMT enabled Deep Insight of MR Images for Brain Tumor Segmentation with Classification Using an Elevated UNet-RESNet Model
PDF (1229KB), PP.33-48
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Author(s)
Surendra Kumar Panda
1,*
Ram Chandra Barik
1
Ganapati Panda
2
Suvamoy Changder
3
Index Terms
Internet of Medical Things, Medical Image Analysis, UNet, Brain Tumor classification, Residual Network
Abstract
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.
Cite This Paper
Surendra Kumar Panda, Ram Chandra Barik, Ganapati Panda, Suvamoy Changder, "An IoMT enabled Deep Insight of MR Images for Brain Tumor Segmentation with Classification Using an Elevated UNet-RESNet Model", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.4, pp. 33-48, 2025. DOI:10.5815/ijigsp.2025.04.03
Reference
[1]S. Ahmad, and P. K. Choudhury, “On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images,” in IEEE Access, vol. 10, pp. 59099-59114, 2022.
[2]F.K. Shoushtari , S. Sina, and A.N. Dehkordi, “Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights” in Physica Medica, Aug 1 2022, 100:51-63.
[3]A. Deshpande, V.V. Estrela, and P. Patavardhan, “The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50”, in Neuroscience Informatics, Volume 1, Issue 4, 2021, 100013, ISSN 2772-5286.
[4]H. Li, A. Li, and M. Wang, “A novel end-to-end brain tumor segmentation method using improved fully convolutional networks”, in Computers in Biology and Medicine, Volume 108, 2019, Pages 150-160, ISSN 0010-4825.
[5]N. Ghassemi, A. Shoeibi, and M. Rouhani, “Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images”, in Biomedical Signal Processing and Control, Volume 57, 2020, 101678, ISSN 1746-8094.
[6]L. Duan, G. Yuan, L. Gong, T. Fu, X. Yang, X. Chen, and J. Zheng, “Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network”, Biomedical Signal Processing and Control, Volume 53, 2019, 101562, ISSN 1746-8094.
[7]S. Kumar, D.P. Mankame, “Optimization driven Deep Convolution Neural Network for brain tumor classification”, in Biocybernetics and Biomedical Engineering, Volume 40, Issue 3, 2020, Pages 1190-1204, ISSN 0208-5216.
[8]M.B. Sahaai, G.R. Jothilakshmi, D. Ravikumar, R. Prasath,and S. Singh, “ResNet-50 based deep neural network using transfer learning for brain tumor classification”, in InAIP Conference Proceedings 2022 May 2 (Vol. 2463, No. 1), AIP Publishing.
[9]R. Rajaragavi,S.P. Rajan, “Optimized UNet Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification”, Intelligent Automation & Soft Computing, Apr 2022, 1;32(1).
[10]S. Anantharajan, G. Shenbagalakshmi, and S. Thavasi , "MRI brain tumor detection using deep learning and machine learning approaches." Measurement: Sensors 31 (2024): 101026.
[11]R. Mehrotra, M.A. Ansari, R. Agrawal, and R.S. Anand, “A Transfer Learning approach for AI-based classification of brain tumors, Machine Learning with Applications”, Volume 2, 2020, 100003, ISSN 2666-8270.
[12]A. G, H. T. J, A. J, A. Bhanu and N. Ig, “Brain Tumor Detection and Classification Using Deep Learning Approaches,” 2023 4th International Conference for Emerging Technology (INCET), Belgaum, India, 2023, pp. 1-6.
[13]J. Zhang et al.,“AResUNet: Attention residual UNet for brain tumor segmentation”, in Symmetry, 2020, 12, 1-15.
[14]S. Khalighi, K. Reddy, A. Midya, K.B. Pandav, A. Madabhushi, and M. Abedalthagafi, "Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment." NPJ precision oncology 8, no. 1 (2024): 80.
[15]S. K. Panda and R. C. Barik, “MR Brain 2D image Tumor and Cyst Classification Approach: an Empirical Analogy,” 2023 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 2023, pp. 1-6.
[16]M.Z. Khaliki, M.S. Bas¸arslan,“ Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN”. Scientifc Reports, Nature Portfolio, 14, 2664 (2024).