Convolutional Neural Networks for Predictive Modeling of Lung Disease

Yingbin Liang, Xiqing Liu, Haohao Xia, Yiru Cang, Zitao Zheng +1 more
8/8/2024
eess.IVcs.AIcs.CV

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Abstract

In this paper, Pro-HRnet-CNN, an innovative model combining HRNet and void-convolution techniques, is proposed for disease prediction under lung imaging. Through the experimental comparison on the authoritative LIDC-IDRI dataset, we found that compared with the traditional ResNet-50, Pro-HRnet-CNN showed better performance in the feature extraction and recognition of small-size nodules, significantly improving the detection accuracy. Particularly within the domain of detecting smaller targets, the model has exhibited a remarkable enhancement in accuracy, thereby pioneering an innovative avenue for the early identification and prognostication of pulmonary conditions.

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Cite this paper

@article{liang2024convolutional,
  title  = {Convolutional Neural Networks for Predictive Modeling of Lung Disease},
  author = {Yingbin Liang and Xiqing Liu and Haohao Xia and Yiru Cang and Zitao Zheng and Yuanfang Yang},
  year   = {2024},
  eprint = {2408.12605},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.12605v1}
}

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