Convolutional Neural Networks for Predictive Modeling of Lung Disease

TL;DR

This paper introduces Pro-HRnet-CNN, a novel deep learning model that combines HRNet and void-convolution techniques to improve lung disease prediction accuracy, especially for small nodules, using the LIDC-IDRI dataset.

Contribution

The paper presents a new CNN model that enhances small nodule detection in lung imaging, outperforming traditional ResNet-50 in feature extraction and recognition tasks.

Findings

Pro-HRnet-CNN outperforms ResNet-50 in detecting small lung nodules.

The model significantly improves early lung disease detection accuracy.

Enhanced feature extraction for small targets in lung imaging.

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.