ArticleComputational and mathematical methods in medicine2022
Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention.Scientific reports · 2024Article
- Computational Methods for Physiological Signal Processing and Data Analysis.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
8 authors at 1 institution in 1 country.
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Abstract
Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors' work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors' diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor's diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.
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