Lung Nodule CT Image Segmentation Model Based on Multiscale Dense Residual Neural Network

Xinying Zhang, Shanshan Kong (), Yang Han, Baoshan Xie and Chunfeng Liu
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Xinying Zhang: The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China
Shanshan Kong: College of Science, North China University of Science and Technology, Tangshan 063210, China
Yang Han: Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China
Baoshan Xie: College of Science, North China University of Science and Technology, Tangshan 063210, China
Chunfeng Liu: The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China

Mathematics, 2023, vol. 11, issue 6, 1-14

Abstract: To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design was added to the feature extraction part, and a local residual design was adopted in the upsampling process. Finally, the effectiveness of the proposed algorithm was evaluated using the LIDC-IDRI lung nodule public dataset. The results showed that the improved algorithm had 7.03%, 14.05%, and 10.43% higher performance than the U-Net segmentation algorithm under the three loss functions of DC, MIOU, and SE, and the accuracy was 2.45% higher compared with that of U-Net. Thus, the proposed method had an effective network structure.

Keywords: U-Net; medical image; segmentation; DenseNet; lung cancer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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