# Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement

arXiv: https://arxiv.org/abs/2005.06189 · HTML: https://mlsanity.com/paper/2005.06189/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 1 of 1)

- [Progressive Semantic Segmentation](https://mlsanity.com/paper/2104.03778/) (2021)

## Abstract (arXiv)

Chest X-ray examination plays an important role in lung disease detection. The more accuracy of this task, the more experienced radiologists are required. After ChestX-ray14 dataset containing over 100,000 frontal-view X-ray images of 14 diseases was released, several models were proposed with high accuracy. In this paper, we develop a work flow for lung disease diagnosis in chest X-ray images, which can improve the average AUROC of the state-of-the-art model from 0.8414 to 0.8445. We apply image preprocessing steps before feeding to the 14 diseases detection model. Our project includes three models: the first one is DenseNet-121 to predict whether a processed image has a better result, a convolutional auto-encoder model for bone shadow exclusion is the second one, and the last is the original CheXNet.