Document Type
Article
Publication Date
2020
Publication Source
2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Abstract
Lung segmentation is a significant step in developing computer-aided diagnosis (CAD) using Chest Radiographs (CRs). CRs are used for diagnosis of the 2019 novel coronavirus disease (COVID-19), lung cancer, tuberculosis, and pneumonia. Hence, developing a Computer-Aided Detection (CAD) system would provide a second opinion to help radiologists in the reading process, increase objectivity, and reduce the workload. In this paper, we present the implementation of our ensemble deep learning model for lung segmentation. This model is based on the original DeepLabV3+, which is the extended model of DeepLabV3. Our model utilizes various architectures as a backbone of DeepLabV3+, such as ResNet18, ResNet50, Mobilenetv2, Xception, and inceptionresnetv2. We improved the encoder module of DeepLabV3+ by adjusting the receptive field of the Spatial Pyramid Pooling (ASPP). We also studied our algorithm's performance on a publicly available dataset provided by Shenzhen Hospital, that contains 566 CRs with manually segmented lungs (ground truth). The experimental result demonstrate the effectiveness of the proposed model on the dataset, achieving an Intersection-Over-Union (IoU, Jaccard Index) score of 0.97 on the test set.
ISBN/ISSN
1550-5219
Document Version
Published Version
Publisher
IEEE
Peer Reviewed
yes
eCommons Citation
Ali, Redha A.; Hardie, Russell C.; and Ragb, Hussin K., "Ensemble Lung Segmentation System Using Deep Neural Networks" (2020). Electrical and Computer Engineering Faculty Publications. 444.
https://ecommons.udayton.edu/ece_fac_pub/444
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/AIPR50011.2020.9425311