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Neural Computing and Applications

Erschienen in:

29.04.2020 | Original Article

Diagnosis method of ultrasonic elasticity image of peripheral lung cancer based on genetic algorithm

verfasst von: Ting Dai, Lujia Ni, Qiang Luo

Erschienen in: Neural Computing and Applications | Ausgabe 24/2020

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Abstract

The current clinical diagnosis of peripheral lung cancer is affected by many factors, which leads to certain uncertainty in the diagnosis results. In order to improve the clinical diagnosis of peripheral lung cancer, based on genetic algorithm, this study constructs a proprietary model for lung cancer detection and diagnosis, designs corresponding training methods and image processing methods in the model, and outputs clinically identifiable diagnostic images for clinical analysis. In order to study the role of genetic algorithm in clinical diagnosis, a comparative trial was designed to compare the clinical diagnosis results with the HRCT method. The experimental results show that the genetic algorithm based on cross-validation optimization has good clinical results in the diagnosis and analysis of peripheral lung cancer and can provide theoretical reference for subsequent related research.

Vorheriger Artikel Ultrasound-elastic-image-assisted diagnosis of pulmonary nodules based on genetic algorithm

Nächster Artikel Automated design of error-resilient and hardware-efficient deep neural networks

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Titel: Diagnosis method of ultrasonic elasticity image of peripheral lung cancer based on genetic algorithm
verfasst von: Ting Dai
Lujia Ni
Qiang Luo
Publikationsdatum: 29.04.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 24/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-04957-w

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