PlumX Metrics
Embed PlumX Metrics

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
  • 3
    Citations
  • 0
    Usage
  • 4
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

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.

Bibliographic Details

Yanan Zhang; Juanjuan Zhao; Yan Qiang; Liye Jia; Wei Wu

Wiley

Computer Science; Mathematics

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know