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Title: Survey of Image Denoising Methods for Medical Image Classification

Abstract

Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.

Authors:
 [1]; ORCiD logo [2]
  1. University of Washington, Seattle
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1648905
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: SPIE Medical Imaging 2020 - Houston, Texas, United States of America - 3/16/2020 4:00:00 AM-3/20/2020 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Michael, Peter, and Yoon, Hong-Jun. Survey of Image Denoising Methods for Medical Image Classification. United States: N. p., 2020. Web. doi:10.1117/12.2549695.
Michael, Peter, & Yoon, Hong-Jun. Survey of Image Denoising Methods for Medical Image Classification. United States. https://doi.org/10.1117/12.2549695
Michael, Peter, and Yoon, Hong-Jun. 2020. "Survey of Image Denoising Methods for Medical Image Classification". United States. https://doi.org/10.1117/12.2549695. https://www.osti.gov/servlets/purl/1648905.
@article{osti_1648905,
title = {Survey of Image Denoising Methods for Medical Image Classification},
author = {Michael, Peter and Yoon, Hong-Jun},
abstractNote = {Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.},
doi = {10.1117/12.2549695},
url = {https://www.osti.gov/biblio/1648905}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Mar 01 00:00:00 EST 2020},
month = {Sun Mar 01 00:00:00 EST 2020}
}

Conference:
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