Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network

Wesley O'Quinn; Rami J. Haddad; David L. Moore

doi:10.1109/ICEICT.2019.8846438

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network

Wesley O'Quinn, Rami J. Haddad, David L. Moore

Electrical & Computer Engineering

Georgia Southern University

Research output: Contribution to book or proceeding › Conference article › peer-review

25 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	763-767
Number of pages	5
ISBN (Electronic)	9781538692981
DOIs	https://doi.org/10.1109/ICEICT.2019.8846438
State	Published - Jan 2019
Event	IEEE International Conference on Electronic Information and Communication Technology - Harbin, China Duration: Jan 20 2019 → Jan 22 2019 Conference number: 2 https://ieeexplore.ieee.org/servlet/opac?punumber=8840948

Publication series

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

Conference

Conference	IEEE International Conference on Electronic Information and Communication Technology
Abbreviated title	IEEE ICEICT
Country/Territory	China
City	Harbin
Period	01/20/19 → 01/22/19
Internet address	https://ieeexplore.ieee.org/servlet/opac?punumber=8840948

Scopus Subject Areas

Information Systems and Management
Safety, Risk, Reliability and Quality
Instrumentation
Information Systems
Computer Networks and Communications
Hardware and Architecture

Keywords

Artificial Intelligence
Deep Learning
Image Processing
Medical Imaging
Pneumonia
Radiograph Processing.

Access to Document

10.1109/ICEICT.2019.8846438

Cite this

O'Quinn, W., Haddad, R. J., & Moore, D. L. (2019). Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network. In Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019 (pp. 763-767). Article 8846438 (Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEICT.2019.8846438

O'Quinn, Wesley ; Haddad, Rami J. ; Moore, David L. / Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network. Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 763-767 (Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019).

@inproceedings{5d30eca0bf924b7bb862c1970ff0ffd5,

title = "Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network",

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.",

keywords = "Artificial Intelligence, Deep Learning, Image Processing, Medical Imaging, Pneumonia, Radiograph Processing.",

author = "Wesley O'Quinn and Haddad, {Rami J.} and Moore, {David L.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; IEEE International Conference on Electronic Information and Communication Technology ; Conference date: 20-01-2019 Through 22-01-2019",

year = "2019",

month = jan,

doi = "10.1109/ICEICT.2019.8846438",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "763--767",

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

address = "United States",

url = "https://ieeexplore.ieee.org/servlet/opac?punumber=8840948",

}

O'Quinn, W, Haddad, RJ & Moore, DL 2019, Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network. in Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019., 8846438, Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019, Institute of Electrical and Electronics Engineers Inc., pp. 763-767, IEEE International Conference on Electronic Information and Communication Technology, Harbin, China, 01/20/19. https://doi.org/10.1109/ICEICT.2019.8846438

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network. / O'Quinn, Wesley; Haddad, Rami J.; Moore, David L.
Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 763-767 8846438 (Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network

AU - O'Quinn, Wesley

AU - Haddad, Rami J.

AU - Moore, David L.

N1 - Conference code: 2

PY - 2019/1

Y1 - 2019/1

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Deep Learning

KW - Image Processing

KW - Medical Imaging

KW - Pneumonia

KW - Radiograph Processing.

UR - http://www.scopus.com/inward/record.url?scp=85074966950&partnerID=8YFLogxK

U2 - 10.1109/ICEICT.2019.8846438

DO - 10.1109/ICEICT.2019.8846438

M3 - Conference article

AN - SCOPUS:85074966950

T3 - Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019

SP - 763

EP - 767

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Conference on Electronic Information and Communication Technology

Y2 - 20 January 2019 through 22 January 2019

ER -

O'Quinn W, Haddad RJ, Moore DL. Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network. In Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 763-767. 8846438. (Proceedings of 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology, ICEICT 2019). doi: 10.1109/ICEICT.2019.8846438

Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network

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