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AI driven feature extraction model for chest cavity spectrum signal visualization

Authors
  • Niu, Haitao1
  • Gu, Jihua1
  • 1 Soochow University,
Type
Published Article
Journal
International Journal of Speech Technology
Publisher
Springer US
Publication Date
Apr 30, 2021
Pages
1–14
Identifiers
DOI: 10.1007/s10772-021-09844-w
PMID: 33967593
PMCID: PMC8090519
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

Lung cancer is the most fatal cancer in the world. Early detection, diagnosis and treatment of lung cancer is an important means to improve the survival rate of lung cancer patients. The early signs of lung cancer are small pulmonary nodules, so early detection and timely treatment of pulmonary nodules are of great significance to save the lives of lung cancer patients. With the progress of medical CT technology, a large number of image data obtained by medical CT examination are increasing, which can provide more organ and tissue information, but also bring a great burden to doctors. Therefore, the detection technology of thoracic spectrum signal is a key point. The chest cavity is a non adjustable resonator with a fixed volume and space, which is located in the chest ribs and below the vocal cords. According to the principle of resonance in physical theory, the resonance characteristics are mainly related to the size of the cavity surrounded by a certain hardness of the outer wall. Therefore, some researchers believe that the volume of the chest cavity is related to the resonance of the chest cavity. Although in recent years, some teams have combined deep learning and machine learning to improve signal feature extraction, which makes signal feature extraction easier and more efficient, most of them are still based on IQ data for signal modulation recognition. Therefore, this paper studies the visual feature extraction model based on artificial intelligence and thoracic echo spectrum, and the experimental results show the effectiveness of this method compared with the latest approaches.

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