"Improving Pneumonia Diagnosis Accuracy viaSystematic Convolutional Neural Network-BasedImage Enhancement"
Location
Allen E. Paulson College of Engineering and Computing (CEC)
Session Format
Oral Presentation
Co-Presenters and Faculty Mentors or Advisors
Dr. Rami J. Haddad, Faculty Advisor
Abstract
Chest X-rays play a significant role in diagnosing pneumonia due to the technology’s cost-effectiveness and rapid development times. Detecting pneumonia in chest X-rays is a challenging process that relies heavily upon the availability of trained radiologists and high-quality imagery. Training qualified interpreters require significant resources, while medical imaging remains prone to a wide variety of deficiencies. Therefore, an automated system for pneumonia diagnosis consisting of three phases is proposed. An initial sorting phase consisting of a trained ResNet-18 convolutional neural network separates the dataset according to the interpretive quality of the images, creating a high and low quality class. The unique image translation capabilities of the CycleGAN network are leveraged in the enhancement phase to translate low-quality images into improved versions. A final ResNet-18 network serves to classify pneumonia in the diagnosis phase. The enhancement system improved mixed quality diagnosis accuracy by 12.1% to 86.7%, with training sets composed of enhanced images achieving an accuracy 15.8% higher than their low-quality counterparts. The system’s generalized method for image augmentation successfully mitigates the deficiencies of low-quality data, allowing for a higher accuracy diagnosis than otherwise possible.
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Recommended Citation
Wang, Ziqi, ""Improving Pneumonia Diagnosis Accuracy viaSystematic Convolutional Neural Network-BasedImage Enhancement"" (2021). GS4 Student Scholars Symposium. 96.
https://digitalcommons.georgiasouthern.edu/research_symposium/2021/2021/96
"Improving Pneumonia Diagnosis Accuracy viaSystematic Convolutional Neural Network-BasedImage Enhancement"
Allen E. Paulson College of Engineering and Computing (CEC)
Chest X-rays play a significant role in diagnosing pneumonia due to the technology’s cost-effectiveness and rapid development times. Detecting pneumonia in chest X-rays is a challenging process that relies heavily upon the availability of trained radiologists and high-quality imagery. Training qualified interpreters require significant resources, while medical imaging remains prone to a wide variety of deficiencies. Therefore, an automated system for pneumonia diagnosis consisting of three phases is proposed. An initial sorting phase consisting of a trained ResNet-18 convolutional neural network separates the dataset according to the interpretive quality of the images, creating a high and low quality class. The unique image translation capabilities of the CycleGAN network are leveraged in the enhancement phase to translate low-quality images into improved versions. A final ResNet-18 network serves to classify pneumonia in the diagnosis phase. The enhancement system improved mixed quality diagnosis accuracy by 12.1% to 86.7%, with training sets composed of enhanced images achieving an accuracy 15.8% higher than their low-quality counterparts. The system’s generalized method for image augmentation successfully mitigates the deficiencies of low-quality data, allowing for a higher accuracy diagnosis than otherwise possible.
