Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

作者：Dewu Yang, Juan Du, Kang Liu, Yan Sui, Junying Wang, Xinghui Gai · 发表于：Technology and Health Care · 年份：2023 · DOI：10.3233/thc-236041 · 被引用次数：6 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、COVID-19 diagnosis using AI

BACKGROUND: Lung cancer is a malignant tumor originating from the bronchial mucosa or glands of the lung. Early lung cancer patients often have no obvious symptoms, but early detection and treatment have an important clinical significance for prognostic effect. Computed tomography (CT) is one of the important means in the diagnosis of lung cancer. In order to better solve the problem of diagnosis efficiency, and reduce the rate of misdiagnosis and missed diagnosis, computer aided diagnosis are employed in the accurate localization and segmentation of pulmonary nodules through imaging diagnostics, image processing technology, and other clinical means. OBJECTIVE: This present study was envisaged to establish an intelligent segmentation model of pulmonary nodules to improve the accuracy of early screening for lung cancer patients. METHODS: Compared with the traditional segmentation model of fully convolutional neural network, the U-Net++ algorithm based on feature-weighted integration (WI-U-Net++) effectively utilized the feature weight information, adopted the adaptive weighted method for weighted integration, and performed an intelligent segmentation of the anatomical structure and image details, which was applied in the auxiliary diagnosis of pulmonary nodules in CT images. Standard chest X-ray phantom was selected as CT scanning objects, and 30 spherical and irregular simulated nodules were built into them, respectively. CT images were collected by setting different tube vol...