Classification of benign and malignant pulmonary nodule based on local-global hybrid network.
J Xray Sci Technol
; 32(3): 689-706, 2024.
Article
in En
| MEDLINE
| ID: mdl-38277335
ABSTRACT
BACKGROUND:
The accurate classification of pulmonary nodules has great application value in assisting doctors in diagnosing conditions and meeting clinical needs. However, the complexity and heterogeneity of pulmonary nodules make it difficult to extract valuable characteristics of pulmonary nodules, so it is still challenging to achieve high-accuracy classification of pulmonary nodules.OBJECTIVE:
In this paper, we propose a local-global hybrid network (LGHNet) to jointly model local and global information to improve the classification ability of benign and malignant pulmonary nodules.METHODS:
First, we introduce the multi-scale local (MSL) block, which splits the input tensor into multiple channel groups, utilizing dilated convolutions with different dilation rates and efficient channel attention to extract fine-grained local information at different scales. Secondly, we design the hybrid attention (HA) block to capture long-range dependencies in spatial and channel dimensions to enhance the representation of global features.RESULTS:
Experiments are carried out on the publicly available LIDC-IDRI and LUNGx datasets, and the accuracy, sensitivity, precision, specificity, and area under the curve (AUC) of the LIDC-IDRI dataset are 94.42%, 94.25%, 93.05%, 92.87%, and 97.26%, respectively. The AUC on the LUNGx dataset was 79.26%.CONCLUSION:
The above classification results are superior to the state-of-the-art methods, indicating that the network has better classification performance and generalization ability.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Solitary Pulmonary Nodule
/
Lung Neoplasms
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Xray Sci Technol
Journal subject:
RADIOLOGIA
Year:
2024
Document type:
Article
Affiliation country: