Multi-level learning based on 3D CT image integrated medical clinic information for accurate diagnosis of pulmonary nodules
Concurrency and Computation: Practice and Experience, ISSN: 1532-0634, Vol: 34, Issue: 17
2022
- 3Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The diagnosis of pulmonary nodules by clinicians depends not only on radiological imaging but also on the patient's own clinical record information and other factors. However, exploring the guiding role of clinical information is a major challenge. In this article, an intelligent personalized diagnosis decision-making model is proposed, which combines radiology images with patient information. First, the 3D image cube of the pulmonary nodule is constructed. Then, a 3D multi-level fusion ResNet is designed to extract the features of the nodule by making full use of the spatial context information. Finally, a kind of classification model based on feature-related analysis was proposed, which fused clinical information features and image features and realized a nonlinear radial basis feature mapping. We tested this method on the public dataset and a cooperation hospital dataset. Experiments show that this method can effectively improve the classification accuracy of unstable nodules at the classification boundary. Our model showed significant improvements in sensitivity, specificity, and accuracy. Meanwhile, compared with other deep learning diagnosis methods, our method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.
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