Electrical & Computer Engineering: Faculty Publications
Automated Diagnosis of Pneumothorax X-ray Images Utilizing Deep Convolutional Neural Network
Document Type
Conference Proceeding
Publication Date
3-28-2020
Publication Title
SoutheastCon 2020 Proceedings
DOI
10.1109/SoutheastCon44009.2020.9249683
Abstract
Pneumothorax is a severe respiratory disease. In this study, an algorithm using a Deep Convolutional Neural Network (DCNN) is proposed to detect visual signs for Pneumothorax within X-ray images and conduct a diagnosis. Detecting and diagnosing Pneumothorax remains challenging despite of its prevalence. The deep residual network ResNet-101 was adapted through transfer learning. A database of 5,302 Pneumothorax radiographs was utilized for training, and a preliminary diagnosis accuracy of 86.26% was obtained. The area under the receiver operating characteristic curve (AUC) was 92.13%.
Recommended Citation
Wang, Ziqi, Rami J. Haddad.
2020.
"Automated Diagnosis of Pneumothorax X-ray Images Utilizing Deep Convolutional Neural Network."
SoutheastCon 2020 Proceedings: Institute of Electrical and Electronics Engineers Inc..
doi: 10.1109/SoutheastCon44009.2020.9249683
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/189
Copyright
See the publisher's copyright and reuse policies.
Comments
Georgia Southern University faculty member, Rami J. Haddad co-authored, "Automated Diagnosis of Pneumothorax X-ray Images Utilizing Deep Convolutional Neural Network."