# A comparative analysis of deep learning models for lung segmentation on X-ray images

arXiv: https://arxiv.org/abs/2404.06455 · HTML: https://mlsanity.com/paper/2404.06455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [On Pre-Trained Image Features and Synthetic Images for Deep Learning](https://mlsanity.com/paper/1710.10710/) (2017)
- [Online 3D Bin Packing with Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2006.14978/) (2020)
- [Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations](https://mlsanity.com/paper/1906.11031/) (2019)
- [Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation](https://mlsanity.com/paper/1803.01199/) (2018)
- [Learning Practically Feasible Policies for Online 3D Bin Packing](https://mlsanity.com/paper/2108.13680/) (2021)
- [Lung Segmentation from Chest X-rays using Variational Data Imputation](https://mlsanity.com/paper/2005.10052/) (2020)
- [Deep Convolutional Neural Networks as Generic Feature Extractors](https://mlsanity.com/paper/1710.02286/) (2017)
- [Online 3D Bin Packing Reinforcement Learning Solution with Buffer](https://mlsanity.com/paper/2208.07123/) (2022)
- [MeROS: SysML-based Metamodel for ROS-based Systems](https://mlsanity.com/paper/2303.08254/) (2023)
- [A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem](https://mlsanity.com/paper/1804.06896/) (2018)
- [An intent-based approach for creating assistive robots' control systems](https://mlsanity.com/paper/2005.12106/) (2020)

## Abstract (arXiv)

Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging. This study evaluates deep learning solutions for this task, ranking existing methods and analyzing their performance under diverse image modifications. Out of 61 analyzed papers, only nine offered implementation or pre-trained models, enabling assessment of three prominent methods: Lung VAE, TransResUNet, and CE-Net. The analysis revealed that CE-Net performs best, demonstrating the highest values in dice similarity coefficient and intersection over union metric.