Blockchain Integrated Neural Networks: A New Frontier in MRI-based Brain Tumor Detection

被引:0
|
作者
Banik, Subrata [1 ]
Barai, Nani Gopal [1 ]
Shamrat, F. M. Javed Mehedi [2 ]
机构
[1] Bangladesh Japan Informat Technol Ltd BJIT Ltd, Dhaka, Bangladesh
[2] Univ Malaya, Dept Comp Syst & Technol, Kuala Lumpur, Malaysia
关键词
Brain tumor; MRI imaging; BrainTumorNet; deep learning; image classification; augmentation; CLASSIFICATION; IMAGES;
D O I
10.14569/IJACSA.2023.0141197
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Brain tumors originating from uncontrolled growth of abnormal cells in the brain, presents a significant challenge in healthcare due to their various symptoms and infrequency. While Magnetic Resonance Imaging (MRI) is essential for accurately identifying and diagnosing malignant tumors, manual interpretation is often complex and sensitive to mistakes. To address this, we introduce BrainTumorNet, a specialized convolutional neural network (CNN) created for MRI-based brain tumor diagnosis. We ensure improved image quality and a robust dataset for model training by including preprocessing approaches involving CLAHE and data augmentation. Additionally, we integrated a blockchain-based data retrieval technology to enhance the security, traceability, and collaboration in MRI data management across several medical institutions. This blockchain framework ensures that MRI data, once input from hospitals, stays immutable and can be safely retrieved based on unique hospital IDs, promoting a trustable environment for data exchange. Performance assessments conducted on multiple MRI datasets showcased BrainTumorNet's commendable proficiency, with accuracy rates of 98.66%, 97.17% and 94.24% on the dataset 1, dataset 2, and dataset 3, respectively. The model's performance was evaluated using a comprehensive set of metrics, including accuracy, specificity, recall, precision, f1-score, and confusion matrix. These measures are essential for evaluating a model's strengths and limits, emphasizing BrainTumorNet's ability to generate accurate and relevant predictions and its effectiveness in determining negative classification. BrainTumorNet's performance was compared with six renowned deep learning InceptionV3, and DenseNet121. Our work highlights BrainTumorNet's potential capabilities in simplifying and boosting the accuracy of MRI-based brain tumor diagnosis while ensuring data integrity and collaboration through blockchain.
引用
收藏
页码:954 / 964
页数:11
相关论文
共 50 条
  • [41] Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences
    Rahimpour, Masoomeh
    Bertels, Jeroen
    Radwan, Ahmed
    Vandermeulen, Henri
    Sunaert, Stefan
    Vandermeulen, Dirk
    Maes, Frederik
    Goffin, Karolien
    Koole, Michel
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (07) : 2153 - 2164
  • [42] MRI Image Based Classification Model for Lung Tumor Detection Using Convolutional Neural Networks
    Kumar, Makineni Siddardha
    Rao, Kasukurthi Venkata
    Kumar, Gona Anil
    TRAITEMENT DU SIGNAL, 2021, 38 (06) : 1837 - 1842
  • [43] Dilated Convolution and YOLOv8 Feature Extraction Network: An Improved Method for MRI-Based Brain Tumor Detection
    Annet Abraham, Lincy
    Palanisamy, Gopinath
    Goutham, Veerapu
    IEEE ACCESS, 2025, 13 : 27238 - 27256
  • [44] Brain Tumor Detection Using Artificial Convolutional Neural Networks
    Irsheidat, Suhib
    Duwairi, Rehab
    2020 11TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 2020, : 197 - 203
  • [45] A Comparative Analysis of Deep Neural Networks for Brain Tumor Detection
    Deepa, P. L.
    Ponraj, Narain
    Sreena, V. G.
    ICSPC'21: 2021 3RD INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICPSC), 2021, : 36 - 40
  • [46] Brain Tumor Detection using MRI Images and Convolutional Neural Network
    Lamrani, Driss
    Cherradi, Bouchaib
    El Gannour, Oussama
    Bouqentar, Mohammed Amine
    Bahatti, Lhoussain
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (07) : 452 - 460
  • [47] MRI-based Brain Healthcare Quotients: A bridge between neural and behavioral analyses for keeping the brain healthy
    Nemoto, Kiyotaka
    Oka, Hiroki
    Fukuda, Hiroki
    Yamakawa, Yoshinori
    PLOS ONE, 2017, 12 (10):
  • [48] A robust MRI-based brain tumor classification via a hybrid deep learning technique
    Shaimaa E. Nassar
    Ibrahim Yasser
    Hanan M. Amer
    Mohamed A. Mohamed
    The Journal of Supercomputing, 2024, 80 : 2403 - 2427
  • [49] A robust MRI-based brain tumor classification via a hybrid deep learning technique
    Nassar, Shaimaa E.
    Yasser, Ibrahim
    Amer, Hanan M.
    Mohamed, Mohamed A.
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (02): : 2403 - 2427
  • [50] Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm
    Bahadure, Nilesh Bhaskarrao
    Ray, Arun Kumar
    Thethi, Har Pal
    JOURNAL OF DIGITAL IMAGING, 2018, 31 (04) : 477 - 489