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Related Concept Videos

Brain Imaging01:14

Brain Imaging

270
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
270

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Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans.

Farah Mohammad1, Saad Al Ahmadi2, Jalal Al Muhtadi1,2

  • 1Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|April 13, 2023
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Summary
This summary is machine-generated.

This study introduces a secure convolutional neural network model using blockchain for precise brain tumor prediction from MRI scans. The method enhances diagnostic accuracy and network security for various tumor types.

Keywords:
blockchainbrain tumordeep learningsecure CNN

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Brain tumor diagnosis and surgical planning using MRI are complex due to tumor variability.
  • Current manual analysis of brain MRIs is time-consuming and prone to errors.
  • Existing methods lack robust security measures for sensitive medical data.

Purpose of the Study:

  • To develop a secure and precise brain tumor prediction model using a convolutional neural network (CNN) integrated with blockchain technology.
  • To enhance the security and integrity of brain MRI data analysis.
  • To improve the accuracy of diagnosing various brain tumors like pituitary tumors, meningiomas, and gliomas.

Main Methods:

  • A pre-trained deep learning model (CNN) was employed for brain MRI analysis after data normalization.
  • Blockchain technology was integrated to secure the CNN model layers against tampering and unauthorized modifications.
  • A Genetic Algorithm was utilized for feature optimization and merging.

Main Results:

  • The proposed blockchain-based CNN model demonstrated competitive performance compared to state-of-the-art methods.
  • The integrated blockchain enhanced the security of the neural network layers, protecting against attacks.
  • The model achieved precise predictions for various brain tumor types.

Conclusions:

  • The integration of blockchain technology with CNNs offers a secure and effective approach for brain tumor prediction from MRI scans.
  • This hybrid model improves diagnostic accuracy and data integrity in neuroimaging.
  • The developed system provides a robust solution for secure and precise analysis of brain MRIs.