1. Level Set Image Feature Detection and Application in COVID-19 Image Feature Knowledge Detection.
- Author
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Ji D, Liu Y, Zhang Q, and Zheng W
- Subjects
- Humans, Diagnostic Imaging, Algorithms, Models, Theoretical, Image Processing, Computer-Assisted methods, Artificial Intelligence, COVID-19 diagnostic imaging
- Abstract
Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models., Competing Interests: The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dongsheng Ji et al.)
- Published
- 2023
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