Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network

Authors

• Wesley O'Quinn, Georgia Southern UniversityFollow
• Rami J. Haddad, Georgia Southern UniversityFollow
• David L. Moore, Georgia Southern University

Document Type

Conference Proceeding

Publication Date

10-23-2019

Publication Title

Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019

DOI

10.1109/ICEICT.2019.8846438

Abstract

Pneumonia is a life-threatening respiratory disease caused by bacterial infection. The goal of this study is to develop an algorithm using Convolutional Neural Networks (CNNs) to detect visual signals for pneumonia in medical images and make a diagnosis. Although Pneumonia is prevalent, detection and diagnosis are challenging. The deep learning network AlexNet was utilized through transfer learning. A dataset consisting of 5659 images was used for training, and a preliminary diagnosis accuracy of 72% was achieved.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored, "Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network."

Recommended Citation

O'Quinn, Wesley, Rami J. Haddad, David L. Moore. 2019. "Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network." Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019: 763-767: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/ICEICT.2019.8846438
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/204

Copyright

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