Honors College Theses
Publication Date
3-25-2021
Major
Electrical Engineering (B.S.)
Document Type and Release Option
Thesis (open access)
Faculty Mentor
Dr. Rami Haddad
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 11,318 images was used for training, and a preliminary diagnosis accuracy of 72% was achieved.
Recommended Citation
O'Quinn, Wesley, "Pneumonia Radiograph Diagnosis Utilizing Deep Learning Network" (2021). Honors College Theses. 561.
https://digitalcommons.georgiasouthern.edu/honors-theses/561
Included in
Bacterial Infections and Mycoses Commons, Other Biomedical Engineering and Bioengineering Commons, Other Electrical and Computer Engineering Commons
