M. V. S. Ramprasad; Md. Zia Ur Rahman

A Holistic Framework for Confidential Brain Tumor Diagnosis over IoMT: HWS-CSIWT and OBTSC-Net Integration

PDF (1222KB), PP.100-123

Views: 0 Downloads: 0

Author(s)

M. V. S. Ramprasad ^1,* Md. Zia Ur Rahman ¹

1. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2026.01.06

Received: 28 Aug. 2025 / Revised: 13 Nov. 2025 / Accepted: 3 Dec. 2025 / Published: 8 Feb. 2026

Index Terms

Brain Tumor Detection, Internet of Medical Things, Data Security, Remote Diagnosis, Medical Information Transmission

Abstract

The development of medical-imaging neurology diagnostics regarding brain tumor detection and classification via the Internet of Medical Things (IoMT) is important. This research proposes a comprehensive framework addressing user privacy concerns by embedding brain tumor information in a cover image through Hybrid Watermarking Steganography (HWS) using Compressive Sensing Integer Wavelet Transform (CSIWT). The watermarked images are securely transmitted over the IoMT, ensuring data integrity. An Inverse CSIWT-HWS system extracts the hidden brain tumor image for diagnosis. The proposed framework incorporates an Optimized Brain Tumor Segmentation and Classification Network (OBTSC-Net) to enhance diagnostic capabilities. This transfer learning model utilizes Attention Generative Adversarial Networks (AGAN) to segment brain tumor areas from the extracted images, Hybrid Greylag Goose Optimization Genetic Algorithm (HGGO-GA) for disease-specific feature extraction from segmented images, and Broad Learning System Neural Network (BLS-NN) for the accurate classification of benign and malignant brain tumors using BraTS-2020 and BraTS-2021 datasets, offering a reliable and secure tool for remote diagnosis. Finally, the proposed HWS-CSIWT method achieved an average Peak Signal-to-Noise Ratio (PSNR) improvement of 12.65% over existing state-of-the-art methods. The proposed AGAN method achieved an average segmentation accuracy (SACC) improvement of 5.63% over existing methods, and the proposed OBTSC-Net achieved an average classification accuracy (CACC) improvement of 2.82% over existing state-of-the-art methods, confirming its enhanced diagnostic capability in brain tumor classification.

Cite This Paper

M. V. S. Ramprasad, Md. Zia Ur Rahman, "A Holistic Framework for Confidential Brain Tumor Diagnosis over IoMT: HWS-CSIWT and OBTSC-Net Integration", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.1, pp.100-123, 2026. DOI:10.5815/ijitcs.2026.01.06

Reference

[1]Solanki, Shubhangi, Uday Pratap Singh, Siddharth Singh Chouhan, and Sanjeev Jain. "Brain Tumor Detection and Classification using Intelligence Techniques: An Overview." IEEE Access (2023).
[2]Asiri, Abdullah A., Ahmad Shaf, Tariq Ali, Muhammad Ahmad Pasha, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani et al. "Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models." Sensors 23, no. 18 (2023): 7913.
[3]Ranjbarzadeh, Ramin, Annalina Caputo, Erfan Babaee Tirkolaee, Saeid Jafarzadeh Ghoushchi, and Malika Bendechache. "Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools." Computers in biology and medicine 152 (2023): 106405.
[4]Griessmair, Michael, Claire Delbridge, Julian Ziegenfeuter, Denise Bernhardt, Jens Gempt, Friederike Schmidt-Graf, Olivia Kertels et al. "Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas." Cancers 15, no. 8 (2023): 2355.
[5]Saurav, Sumeet, Ayush Sharma, Ravi Saini, and Sanjay Singh. "An attention-guided convolutional neural network for automated brain tumour classification from MRI." Neural Computing and Applications 35, no. 3 (2023): 2541–2560.
[6]Tang, Chaosheng, Bin Li, Junding Sun, Shui-Hua Wang, and Yu-Dong Zhang. "GAM-SpCaNet: Gradient awareness minimisation-based spinal convolution attention network for brain tumour classification." Journal of King Saud University-Computer and Information Sciences 35, no. 2 (2023): 560–575.
[7]Mijwil, Maad M., Ruchi Doshi, Kamal Kant Hiran, Omega John Unogwu, and Indu Bala. "MobileNetV1-Based Deep Learning Model for Accurate Brain Tumor Classification." Mesopotamian Journal of Computer Science 2023 (2023): 32-41.
[8]Sharma, Arpit Kumar, Amita Nandal, Arvind Dhaka, Liang Zhou, Adi Alhudhaif, Fayadh Alenezi, and Kemal Polat. "Brain tumour classification using the modified ResNet50 model based on transfer learning." Biomedical Signal Processing and Control 86 (2023): 105299.
[9]Demir, Fatih, et al. "Improving brain tumour classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data." Biomedical Signal Processing and Control 81 (2023): 104424.
[10]Kurdi, Sarah Zuhair, Mohammed Hasan Ali, Mustafa Musa Jaber, Tanzila Saba, Amjad Rehman, and Robertas Damaševičius. "Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks." Journal of Personalized Medicine 13, no. 2 (2023): 181.
[11]Mehnatkesh, Hossein, Seyed Mohammad Jafar Jalali, Abbas Khosravi, and Saeid Nahavandi. "An intelligent, driven deep residual learning framework for brain tumour classification using MRI images." Expert Systems with Applications 213 (2023): 119087.
[12]Sharif, Muhammad Irfan, Jian Ping Li, Muhammad Attique Khan, Seifedine Kadry, and Usman Tariq. "M3BTCNet: multi-model brain tumour classification using metaheuristic deep neural network features optimisation." Neural Computing and Applications 36, no. 1 (2024): 95–110.
[13]Patil, Deepak O., and Satish T. Hamde. "A new enhanced local optimal-oriented pattern descriptor using steerable wavelet transform for MRI brain tumour classification." Iranian Journal of Science and Technology, Transactions of Electrical Engineering 47, no. 1 (2023): 369–384.
[14]Kishanrao, Salve Amrapali, and Kalpana C. Jondhale. "An improved grade-based MRI brain tumour classification using hybrid DCNN-DH framework." Biomedical Signal Processing and Control 85 (2023): 104973.
[15]Haq, Amin ul, Jian Ping Li, Rajesh Kumar, Zafar Ali, Inayat Khan, M. Irfan Uddin, and Bless Lord Y. Agbley. "MCNN: a multi-level CNN model for the classification of brain tumours in IoT-healthcare system." Journal of Ambient Intelligence and Humanized Computing 14, no. 5 (2023): 4695–4706.
[16]Rajeena, P. P., & Sivakumar, R. (2023). Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier. Intelligent Automation & Soft Computing, 35(3).
[17]Abd El-Wahab, Basant S., Mohamed E. Nasr, Salah Khamis, and Amira S. Ashour. "BTC-fCNN: Fast Convolution Neural Network for Multiclass Brain Tumor Classification." Health Information Science and Systems 11, no. 1 (2023): 3.
[18]Özkaraca, Osman, Okan İhsan Bağrıaçık, Hüseyin Gürüler, Faheem Khan, Jamil Hussain, Jawad Khan, and Umm E. Laila. "Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images." Life 13, no. 2 (2023): 349.
[19]Singh, Ravendra, and Bharat Bhushan Agarwal. "An automated brain tumour classification in MR images using an enhanced convolutional neural network." International Journal of Information Technology 15, no. 2 (2023): 665–674.
[20]Muezzinoglu, Taha, Nursena Baygin, Ilknur Tuncer, Prabal Datta Barua, Mehmet Baygin, Sengul Dogan, Turker Tuncer, Elizabeth Emma Palmer, Kang Hao Cheong, and U. Rajendra Acharya. "PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images." Journal of Digital Imaging (2023): 1-15.
[21]Zhu, Ziquan, M. Khan, Shui-Hua Wang, and Y. Zhang. "RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification." Cmc-Comput. Mater. Contin 74 (2023): 101–111.
[22]Asif, Sohaib, Ming Zhao, Fengxiao Tang, and Yusen Zhu. "An enhanced deep learning method for multiclass brain tumour classification using deep transfer learning." Multimedia Tools and Applications (2023): 1–28.
[23]Nanda, Aparajita, Ram Chandra Barik, and Sambit Bakshi. "SSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique." Biomedical Signal Processing and Control 81 (2023): 104356.
[24]Kumar, Sunil, and Dilip Kumar. "Human brain tumour classification and segmentation using CNN." Multimedia Tools and Applications 82, no. 5 (2023): 7599-7620.
[25]Haq, Mohd Anul, Ilyas Khan, Ahsan Ahmed, Sayed M. Eldin, Ali Alshehri, and Nivin A. Ghamry. "DCNNBT: A novel deep convolution neural network-based brain tumour classification model." Fractals (2023): 2340102.
[26]Hossain, Amran, Mohammad Tariqul Islam, Sharul Kamal Abdul Rahim, Md Atiqur Rahman, Tawsifur Rahman, Haslina Arshad, Amit Khandakar, Mohamed Arslane Ayari, and Muhammad EH Chowdhury. "A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images." Biosensors 13, no. 2 (2023): 238.
[27]Shahin, Ahmed I., Saleh Aly, and Walaa Aly. "A novel multiclass brain tumour classification method based on unsupervised PCANet features." Neural Computing and Applications 35, no. 15 (2023): 11043–11059.
[28]Emam, Marwa M., Nagwan Abdel Samee, Mona M. Jamjoom, and Essam H. Houssein. "Optimised deep learning architecture for brain tumour classification using improved Hunger Games Search Algorithm." Computers in Biology and Medicine 160 (2023): 106966.
[29]Shahin, Ahmed I., Walaa Aly, and Saleh Aly. "MBTFCN: A novel modular fully convolutional network for MRI brain tumour multi-classification." Expert Systems with Applications 212 (2023): 118776.
[30]Athisayamani, Suganya, Robert Singh Antonyswamy, Velliangiri Sarveshwaran, Meshari Almeshari, Yasser Alzamil, and Vinayakumar Ravi. "Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimised Feature Dimension Reduction for MRI Brain Tumor Classification." Diagnostics 13, no. 4 (2023): 668.
[31]Aloraini, Mohammed, Asma Khan, Suliman Aladhadh, Shabana Habib, Mohammed F. Alsharekh, and Muhammad Islam. "Combining the Transformer and Convolution for Effective Brain Tumor Classification Using MRI Images." Applied Sciences 13, no. 6 (2023): 3680.
[32]Lavanya, S., S. V. Annlin Jeba, P. Bhuvaneswari, and Francis H. Shajin. "Support vector machine classifier optimised with a seagull optimisation algorithm for brain tumour classification." Concurrency and Computation: Practice and Experience 35, no. 1 (2023): e7396.
[33]Zulfiqar, Fatima, Usama Ijaz Bajwa, and Yasar Mehmood. "Multiclass classification of brain tumour types from MR images using EfficientNets." Biomedical Signal Processing and Control 84 (2023): 104777.
[34]Asiri, Abdullah A., Ahmad Shaf, Tariq Ali, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani, Khlood M. Mehdar et al. "Brain tumour detection and classification using fine-tuned CNN with ResNet50 and U-Net model: A study on TCGA-LGG and TCIA dataset for MRI applications." Life 13, no. 7 (2023): 1449.
[35]Xiao, Guanghua, Huibin Wang, Jie Shen, Zhe Chen, Zhen Zhang, and Xiaomin Ge. "Contrastive learning with dynamic weighting and jigsaw augmentation for brain tumour classification in MRI." Neural Processing Letters (2023): 1–29.
[36]Geetha, M., V. Srinath, J. Janet, and S. Sumathi. "Hybrid Archimedes sine cosine optimisation enabled deep learning for multi-level brain tumour classification using MRI images." Biomedical Signal Processing and Control 87 (2024): 105419.
[37]Khan, Farhana, Yonis Gulzar, Shahnawaz Ayoub, Muneer Majid, Mohammad Shuaib Mir, and Arjumand Bano Soomro. "Least Square-Support Vector Machine Based Brain Tumor Classification System with Multi Model Texture Features." Frontiers in Applied Mathematics and Statistics 9 (2023): 1324054.
[38]Cinar, Necip, Mehmet Kaya, and Buket Kaya. "A novel convolutional neural network‐based approach for brain tumour classification using magnetic resonance images." International Journal of Imaging Systems and Technology 33, no. 3 (2023): 895-908.
[39]Asiri, Abdullah A., Ahmad Shaf, Tariq Ali, Muhammad Aamir, Ali Usman, Muhammad Irfan, Hassan A. Alshamrani, Khlood M. Mehdar, Osama M. Alshehri, and Samar M. Alqhtani. "Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images." Intelligent Automation & Soft Computing 36, no. 1 (2023).
[40]Ch, Rupa, Gautam Srivastava, and Reddy Gadekallu. "ECDSA-based tamper detection in medical data using a watermarking technique." International Journal of Cognitive Computing in Engineering 5 (2024): 78-87.
[41]Palani, A., & Loganathan, A. (2024). Semi-blind watermarking using convolutional attention-based turtle shell matrix for tamper detection and recovery of medical images. Expert Systems with Applications, 238, 121903.
[42]Burjulius, Renol, Dini Rohani, and Sonty Lena. "Encryption of Information on Brain Tumor Images Using Vigenere Cipher Algorithm and Least Significant Bits." Journal of Applied Intelligent System 8, no. 3 (2023): 287-297.
[43]Shi, Hui, Kexun Yan, Jianing Geng, and Yonggong Ren. "A cross-embedding based medical image tamper detection and self-recovery watermarking scheme." Multimedia Tools and Applications (2023): 1-42.
[44]Ingaleshwar, Subodh, and Nagaraj V. Dharwadkar. "Water chaotic fruit fly optimisation-based deep convolutional neural network for image watermarking using wavelet transform." Multimedia Tools and Applications 82, no. 14 (2023): 21957–21981.
[45]Talukder, Md Alamin, Md Manowarul Islam, Md Ashraf Uddin, Arnisha Akhter, Md Alamgir Jalil Pramanik, Sunil Aryal, Muhammad Ali Abdulllah Almoyad, Khondokar Fida Hasan, and Mohammad Ali Moni. "An efficient deep learning model to categorise brain tumour using reconstruction and fine-tuning." Expert Systems with Applications (2023): 120534.
[46]Khan, Muhammad Attique, Awais Khan, Majed Alhaisoni, Abdullah Alqahtani, Shtwai Alsubai, Meshal Alharbi, Nazir Ahmed Malik, and Robertas Damaševičius. "Multimodal brain tumour detection and classification using deep saliency map and improved dragonfly optimisation algorithm." International Journal of Imaging Systems and Technology 33, no. 2 (2023): 572–587.
[47]Balamurugan, T., and E. Gnanamanoharan. "Brain tumour segmentation and classification using hybrid deep CNN with LuNetClassifier." Neural Computing and Applications 35, no. 6 (2023): 4739-4753.
[48]Gómez-Guzmán, Marco Antonio, Laura Jiménez-Beristaín, Enrique Efren García-Guerrero, Oscar Roberto López-Bonilla, Ulises Jesús Tao-Perez, José Jaime Esqueda-Elizondo, Kenia Palomino-Vizcaino, and Everardo Inzunza-González. "Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks." Electronics 12, no. 4 (2023): 955.
[49]Rahman, Takowa, and Md Saiful Islam. "MRI brain tumour detection and classification using parallel deep convolutional neural networks." Measurement: Sensors 26 (2023): 100694.
[50]Alyami, Jaber, Amjad Rehman, Fahad Almutairi, Abdul Muiz Fayyaz, Sudipta Roy, Tanzila Saba, and Alhassan Alkhurim. "Tumor Localisation and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm." Cognitive Computation (2023): 1-11.

International Journal of Information Technology and Computer Science (IJITCS)