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Related Concept Videos

Brain Imaging01:14

Brain Imaging

203
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...
203

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Related Experiment Video

Updated: May 29, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
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Context aware machine learning techniques for brain tumor classification and detection - A review.

Usman Amjad1, Asif Raza2, Muhammad Fahad3

  • 1NED University of Engineering and Technology, Karachi, Pakistan.

Heliyon
|February 5, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning, particularly Convolutional Neural Networks (CNN), enhances brain tumor classification and segmentation using MRI and histopathology. These AI methods show promise for precise diagnosis and improved treatment planning.

Keywords:
CNNDeep learningHistologyK-MEANS clusteringMRIMachine learningSurvival predictionTumor segmentation

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Area of Science:

  • Artificial Intelligence in Oncology
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Machine learning (ML) offers significant potential for precise diagnosis, prediction, and classification of brain tumors in acute medical care.
  • Malignant gliomas are aggressive brain tumors; understanding their genetic abnormalities aids in classification and prognosis.
  • Recent genetic insights into brain tumors support improved histopathological and biological characterization.

Purpose of the Study:

  • To review and predict gene alterations using ML techniques, correlating them with various tumor types.
  • To explore the prediction of genetic mutations and structures via advanced ML, focusing on multi-modal MRI and histopathology data.
  • To utilize Convolutional Neural Networks (CNN) for image processing and analysis in brain tumor classification.

Main Methods:

  • Review of recent advancements in MRI and histology image processing for tumor classification (Glioma, Meningioma, Pituitary, Oligodendroglioma, Astrocytoma).
  • Application of various neural network architectures to address challenges in tumor classification, segmentation, datasets, and modalities.
  • Competitive analysis of CNN performance and implication of K-MEANS clustering for predicting genetic structure and molecular alterations.

Main Results:

  • CNN and K-Nearest Neighbors (KNN) effectively classify and segment tumors by extracting image-based features, overcoming image analysis challenges.
  • CNN algorithms outperform other methods on public datasets for tumor classification and segmentation.
  • The study highlights the potential of CNNs for precise tumor diagnosis and treatment planning.

Conclusions:

  • ML, particularly CNN and Support Vector Machine (SVM) algorithms, shows significant potential for accurate brain tumor diagnosis and classification using imaging and histopathology.
  • Advancements in ML are expected to improve the accuracy and efficiency of intra-operative tumor diagnosis.
  • Further research in ML for neuro-oncology can enhance treatment strategies and patient outcomes.