mlsanityData to 13 Oct 2026

Research on Feature Extraction Data Processing System For MRI of Brain Diseases Based on Computer Deep Learning

arXiv 2406.16981 ↗First posted 23 Jun 2024eess.IV cs.AI cs.LGcited by 5

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Abstract (arXiv)

Most of the existing wavelet image processing techniques are carried out in the form of single-scale reconstruction and multiple iterations. However, processing high-quality fMRI data presents problems such as mixed noise and excessive computation time. This project proposes the use of matrix operations by combining mixed noise elimination methods with wavelet analysis to replace traditional iterative algorithms. Functional magnetic resonance imaging (fMRI) of the auditory cortex of a single subject is analyzed and compared to the wavelet domain signal processing technology based on repeated times and the world's most influential SPM8. Experiments show that this algorithm is the fastest in computing time, and its detection effect is comparable to the traditional iterative algorithm. However, this has a higher practical value for the processing of FMRI data. In addition, the wavelet analysis method proposed signal processing to speed up the calculation rate.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 4 of its 25 references that are in the map. First posted on arXiv 23 Jun 2024.

Builds on

4 of 25 references.

References · 4

  1. 2023NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Spacecited by 89
  2. 2023Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNNcited by 35
  3. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  4. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15

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Cited by year

Citing papers per year: 2024: 5.

5 papers.

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