# Convolutional Neural Networks for Predictive Modeling of Lung Disease

arXiv: https://arxiv.org/abs/2408.12605 · HTML: https://mlsanity.com/paper/2408.12605/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation](https://mlsanity.com/paper/1505.03046/) (2015)
- [Application of Multimodal Fusion Deep Learning Model in Disease Recognition](https://mlsanity.com/paper/2406.18546/) (2024)
- [Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data](https://mlsanity.com/paper/2406.00016/) (2024)
- [Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Data](https://mlsanity.com/paper/2406.18547/) (2024)
- [Advanced Financial Fraud Detection Using GNN-CL Model](https://mlsanity.com/paper/2407.06529/) (2024)
- [An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation](https://mlsanity.com/paper/2406.01605/) (2024)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network](https://mlsanity.com/paper/2406.08837/) (2024)
- [Application of Computer Deep Learning Model in Diagnosis of Pulmonary Nodules](https://mlsanity.com/paper/2406.13205/) (2024)
- [Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique](https://mlsanity.com/paper/2406.18549/) (2024)
- [Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning](https://mlsanity.com/paper/2406.08864/) (2024)
- [Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technology](https://mlsanity.com/paper/2406.16982/) (2024)
- [Research on Optimization of Natural Language Processing Model Based on Multimodal Deep Learning](https://mlsanity.com/paper/2406.08838/) (2024)
- [Research on Feature Extraction Data Processing System For MRI of Brain Diseases Based on Computer Deep Learning](https://mlsanity.com/paper/2406.16981/) (2024)

## Built on by (most cited first, 7 of 7)

- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation](https://mlsanity.com/paper/2411.03572/) (2024)
- [Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm](https://mlsanity.com/paper/2410.12259/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Automated Genre-Aware Article Scoring and Feedback Using Large Language Models](https://mlsanity.com/paper/2410.14165/) (2024)
- [Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems](https://mlsanity.com/paper/2410.14167/) (2024)

## Abstract (arXiv)

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.