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.
@article{arxiv.2408.12605,
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},
journal= {arXiv preprint arXiv:2408.12605},
year = {2024}
}