Multi-Label Chest X-Ray Classification via Deep Learning

Aravind Sasidharan Pillai
University of Illinois Urbana-Champaign, Champaign, IL, USA.

DOI: 10.4236/jilsa.2022.144004

Article :

https://www.scirp.org/journal/paperinformation.aspx?paperid=120911

How to cite this paper:

Pillai, A.S. (2022) Multi-Label Chest X-Ray Classification via Deep Learning. Journal of Intelligent Learning Systems and Applications , 14, 43-56. https://doi.org/10.4236/jilsa.2022.144004

Authors

• Aravind Pillai

Our goal in this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. For training, we used dataset consisting of 224,316 chest radiographs of 65,240 patients who underwent a radiographic examination from Stanford University Medical Center between October 2002 and July 2017.

Experiment set up

We used AWS Deep Learning AMI (Ubuntu 18.04), g4dn.2xlarge for training and prediction.

Execute Notebook

• source activate pytorch_latest_p37
• jupyter nbconvert --to=notebook --inplace --ExecutePreprocessor.enabled=True cs-598-multi-labelchest-x-ray-classification.ipynb > new.log 2>&1 & >> disown