REVIEW 1 cited by
Convolutional Neural Networks for Predictive Modeling of Lung Disease
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification
A report claiming 95.12% few-shot accuracy on Mini-ImageNet from a self-supervised ResNet-101 pipeline, with insufficient experimental evidence.
Discussion (0). Continue with ORCID to comment.