Mr. Peter F. Michael Profile

Peter F. Michael

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Proceedings Article | 16 March 2020 Paper

Survey of image denoising methods for medical image classification

Peter Michael, Hong-Jun Yoon

Proceedings Volume 11314, 113143R (2020) https://doi.org/10.1117/12.2549695

KEYWORDS: Denoising, Image classification, Medical imaging, Digital filtering, Electronic filtering, Performance modeling, Image denoising, Data modeling, Image filtering, Filtering (signal processing)

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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.

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