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Related Experiment Video

Updated: Oct 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python.

Moumin A K Mohamed1,2, Alexander Alamri3,4, Brandon Smith5

  • 1Department of Neurosurgery, Royal London Hospital, London, UK. mouminakm@gmail.com.

Acta Neurochirurgica. Supplement
|December 4, 2021
PubMed
Summary

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This summary is machine-generated.

This chapter details acquiring and storing medical imaging data, preparing it for machine learning, and building a convolutional neural network for analysis.

Area of Science:

  • Medical Imaging Analysis
  • Machine Learning in Healthcare

Background:

  • Medical imaging is crucial for diagnosis.
  • Efficient data handling is essential for AI development.

Purpose of the Study:

  • To outline the complete workflow for medical imaging data in machine learning.
  • To provide a practical guide for researchers and practitioners.

Main Methods:

  • Describing medical imaging data acquisition and storage protocols.
  • Detailing step-by-step extraction and preparation of imaging data for machine learning.
  • Illustrating the construction and evaluation of a convolutional neural network.

Main Results:

  • A comprehensive protocol for medical imaging data handling was established.
Keywords:
CNNConvolutional neural networkDICOMHead injuryMedical imagingNeurotraumaTBI

Related Experiment Videos

Last Updated: Oct 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
  • A clear methodology for preparing data for machine learning algorithms was presented.
  • The process of building and assessing a convolutional neural network was demonstrated.

    • Conclusions:

    • The described workflow facilitates the application of machine learning in medical imaging.
    • This chapter serves as a foundational guide for developing AI-driven medical imaging solutions.