Abstract
Background: High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. Methods: In this study, five different pixel interpolation algorithms (nearest neighbor, bilinear, Hamming window, bicubic, and Lanczos interpolation) were used for image downsampling to investigate their effects on the prediction accuracy of a CNN. Chest X-ray images from the NIH public dataset were examined by downsampling 10 patterns. Results: The accuracy improved with a decreasing image size, and the best accuracy was achieved at 64 × 64 pixels. Among the interpolation methods, bicubic interpolation obtained the highest accuracy, followed by the Hamming window.
Highlights
Introductionconvolutional neural networks (CNNs) are widely used for medical imaging applications, such as detecting lung tumors in computed tomography images, detecting breast cancer in mammograms, and predicting the risk for cardiovascular disease based on retinal fundus photographs [2] [3] [4]
In this study, five different pixel interpolation algorithms were used for image downsampling to investigate their effects on the prediction accuracy of a convolutional neural networks (CNNs)
CNNs are widely used for medical imaging applications, such as detecting lung tumors in computed tomography images, detecting breast cancer in mammograms, and predicting the risk for cardiovascular disease based on retinal fundus photographs [2] [3] [4]
Summary
CNNs are widely used for medical imaging applications, such as detecting lung tumors in computed tomography images, detecting breast cancer in mammograms, and predicting the risk for cardiovascular disease based on retinal fundus photographs [2] [3] [4]. Their adoption in the clinical field makes ensuring their accuracy imperative. Various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. Bicubic interpolation obtained the highest accuracy, followed by the Hamming window
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