@inproceedings{4aef7f00f2c94ab58e09e79e8862f3b5,
title = "Automated Diagnosis of Pneumothorax X-ray Images Utilizing Deep Convolutional Neural Network",
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%.",
keywords = "Convolutional Neural Networks, Deep Learning, Medical Imaging, Pneumothorax, Radiograph Processing",
author = "Ziqi Wang and Haddad, {Rami J.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE SoutheastCon, SoutheastCon 2020 ; Conference date: 28-03-2020 Through 29-03-2020",
year = "2020",
month = mar,
day = "28",
doi = "10.1109/SoutheastCon44009.2020.9249683",
language = "English",
series = "Conference Proceedings - IEEE SOUTHEASTCON",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE SoutheastCon 2020, SoutheastCon 2020",
address = "United States",
}