ResDSda_U-Net: A Novel U-Net-Based Residual Network for Segmentation of Pulmonary Nodules in Lung CT Images

Ji, Zhanlin; Zhao, Ziqi; Zeng, Xinyi; Wang, Jingkun; Zhao, Li; Zhang, Xueji; Ганчев, Иван

doi:10.1109/access.2023.3305270

Cited by 4 publications

(1 citation statement)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early diagnosis and accurate classification of nodules are crucial for devising treatment plans and improving patient prognosis. Therefore, enhancing the accuracy of lung nodule detection and classification is significant for clinical diagnosis and patient care [5,6].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Study on Deep Learning-Based CT Image Analysis for Lung Nodule Detection and Classification

Cheng,

Li,

et al. 2024

View full text Add to dashboard Cite

Early detection and accurate classification of lung nodules are crucial for the treatment of lung cancer. With the widespread application of deep learning technologies in medical imaging analysis, significant progress has been made in the automatic detection and classification of lung nodules from computed tomography (CT) images. However, existing deep learning approaches often face challenges with limited annotated data and generalization across diverse datasets. To address these challenges, this study introduces two innovative methods: a domain-adaptive adversarial network for joint segmentation of lung CT images to enhance model generalization, and an improved deep propagation generation network (DPGN) for few-shot classification of lung CT images to reduce reliance on extensive annotated data. Through these methods, this research aims to improve the accuracy of lung nodule detection and classification, providing more reliable support for clinical diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%