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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
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Convolution Properties I01:20

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Convolution computations can be simplified by utilizing their inherent properties.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Brain Tumour Segmentation Using Convolutional Neural Network with Tensor Flow.

M Malathi1, P Sinthia1

  • 1Saveetha Engineering College,Chennai, India.

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|July 28, 2019
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Summary
This summary is machine-generated.

This study presents an automated method for segmenting brain tumors using convolutional neural networks, improving diagnostic speed and accuracy for aggressive gliomas. Early detection through precise tumor extent determination enhances patient survival rates.

Keywords:
Magnetic resonance imagingSegmentationbrain tumourconvolutional neural network

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Accurate determination of brain tumor extent is crucial for treatment planning and prognosis.
  • Gliomas are aggressive brain tumors with low survival rates, necessitating efficient diagnostic tools.
  • Manual segmentation of brain tumors from Magnetic Resonance Imaging (MRI) is time-consuming and operator-dependent.

Purpose of the Study:

  • To develop a fully automatic brain tumor segmentation method using convolutional neural networks (CNNs).
  • To improve the speed and accuracy of brain tumor segmentation for enhanced diagnosis and treatment planning.
  • To segment high-grade gliomas from MRI data for better patient outcomes.

Main Methods:

  • Utilized a convolutional neural network (CNN) for fully automatic brain tumor segmentation.
  • Employed the BRATS 2015 database containing high-grade glioma brain images.
  • Implemented the segmentation model using TensorFlow within the Anaconda framework.

Main Results:

  • Successfully segmented brain tumors into four distinct classes: edema, non-enhancing tumor, enhancing tumor, and necrotic tumor.
  • Demonstrated the capability to differentiate between healthy tissues and various tumor regions.
  • The automated segmentation provides essential data for timely diagnosis and treatment planning.

Conclusions:

  • Fully automatic brain tumor segmentation using CNNs is feasible and effective.
  • This approach can significantly aid in the early diagnosis and quantitative evaluation of brain tumors.
  • Improved segmentation accuracy and speed can contribute to better patient survival rates for aggressive gliomas.