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Related Concept Videos

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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.
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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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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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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep

Rajesh Kumar Thangavel1, Antony Allwyn Sundarraj2, Jayabrabu Ramakrishnan3

  • 1Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.

Electromagnetic Biology and Medicine
|January 21, 2025
PubMed
Summary
This summary is machine-generated.

A novel Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm (PACDNN-COA-BTI-MRI) improves brain tumor identification from MRI scans. This advanced method enhances accuracy and recall, outperforming existing techniques for better diagnostic outcomes.

Keywords:
Brain tumorcoati optimization algorithmmultivariate fast iterative filteringphase-aware composite deep neural network and self-supervised nonlinear transform

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Brain tumors disrupt normal brain function, leading to sensory, motor, and cognitive deficits.
  • Malignant tumors grow rapidly and invade surrounding tissues, while benign tumors grow slowly.
  • Accurate and early detection of brain tumors is crucial for effective treatment planning.

Purpose of the Study:

  • To introduce a novel deep learning model, PACDNN-COA-BTI-MRI, for enhanced brain tumor identification using MRI.
  • To evaluate the performance of the proposed model against existing state-of-the-art methods.
  • To improve the accuracy and reliability of automated brain tumor detection.

Main Methods:

  • Utilized a brain tumor dataset for Magnetic Resonance Imaging (MRI) analysis.
  • Pre-processed images using Multivariate Fast Iterative Filtering (MFIF) to reduce overfitting.
  • Extracted essential features (model, shape, intensity) using Self-Supervised Nonlinear Transform (SSNT).
  • Implemented the Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm (PACDNN-COA-BTI-MRI) in MATLAB.

Main Results:

  • The PACDNN-COA-BTI-MRI model demonstrated significant improvements in performance metrics.
  • Achieved higher accuracy (16.7-30.5%), recall (19.9-30.1%), and precision (16.7-30.8%) compared to existing methods.
  • Outperformed MRI-DLA-ECBT, MRI-BTD-CDMLT, and MRI-BTID-CNN techniques in key performance indicators.

Conclusions:

  • The PACDNN-COA-BTI-MRI approach offers a superior method for brain tumor identification from MRI data.
  • The proposed model shows potential for improving diagnostic accuracy and patient outcomes.
  • This research highlights the effectiveness of integrating advanced deep learning algorithms with optimized feature extraction for medical image analysis.