Title: Visual segmentation of the diagnosis image of pulmonary nodules with vascular adhesion based on convolution neural network

Authors: Yingying Zhao; Chunxia Zhao; Xuekun Song

Addresses: School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China ' School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China ' School of Information Technology, Henan University of Chinese Medicine, Zhengzhou, 450046, China

Abstract: Because of the low grey value of the background area in the diagnosis image of pulmonary nodules with vascular adhesions, the traditional visual segmentation method is weak for image feature recognition, which results in the unsatisfactory visual segmentation effect. A visual segmentation method based on convolution neural network is proposed for the diagnosis image of pulmonary nodules with vascular adhesions. Three modes are added to the original convolution neural network through the filter, and the convolution neural network is used to fuse the diagnosis image of pulmonary nodules with duct adhesion. The fusion results were processed by the fuzzy c-means method to complete the visual segmentation of the diagnosis image of pulmonary nodules with vascular adhesion. The simulation results show that the proposed method can quickly and accurately complete the visual segmentation of the diagnosis image of pulmonary nodules with vascular adhesion, and has strong adaptability.

Keywords: convolution neural network; pulmonary nodules with vascular adhesion; visual segmentation of diagnosis image.

DOI: 10.1504/IJICT.2023.128715

International Journal of Information and Communication Technology, 2023 Vol.22 No.2, pp.147 - 161

Received: 24 Dec 2020
Accepted: 28 Jan 2021
Published online: 02 Feb 2023 *

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