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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Brain Imaging01:14

Brain Imaging

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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...
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Related Experiment Video

Updated: Sep 9, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Computer vision based efficient segmentation and classification of multi brain tumor using computed tomography

Aqib Ali1, Xinde Li2,3, Wali Khan Mashwani4

  • 1Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Nanjing, 210096, China.

Scientific Reports
|September 1, 2025
PubMed
Summary
This summary is machine-generated.

Computer vision effectively classifies six brain tumor types from CT scans. Multilayer perceptron achieved 97.83% accuracy, demonstrating the potential of these techniques in neuro-oncology diagnostics.

Keywords:
ABTFCSBrain tumorComputer visionMultilayer perceptronOptimized statistical multi-features

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Brain tumors pose a significant diagnostic challenge.
  • Accurate classification of tumor types is crucial for effective treatment planning.
  • Computed tomography (CT) scans are a primary imaging modality for brain tumor detection.

Purpose of the Study:

  • To evaluate the effectiveness of computer vision (CV) techniques for classifying six types of brain tumors (benign and malignant) using CT scans.
  • To develop and validate a robust framework for automated brain tumor classification.
  • To compare the performance of various CV classifiers for this task.

Main Methods:

  • A dataset of 900 CT scans was pre-processed, including noise reduction and region of interest (ROI) extraction using automated binary threshold-based fuzzy c-means segmentation (ABTFCS).
  • 135 statistical multi-features were extracted from each ROI, and an optimized set of 12 features was selected using correlation-based feature selection.
  • Five CV classifiers (MLP, BayesNet, PART, random tree, randomizable filtered classifier) were evaluated using 10-fold cross-validation.

Main Results:

  • The pre-processing and feature extraction pipeline generated a refined dataset for classification.
  • Feature selection identified the most relevant statistical attributes for distinguishing tumor types.
  • Multilayer perceptron (MLP) achieved the highest classification accuracy of 97.83% after hyperparameter tuning.

Conclusions:

  • Computer vision techniques, particularly MLP, demonstrate high effectiveness in classifying brain tumors from CT scans.
  • The proposed framework offers a promising automated approach for brain tumor diagnosis.
  • Further research can explore larger datasets and advanced CV models for improved diagnostic accuracy.