Patchwise U-Net Based Semantic Segmentation of COVID-19 Infection from Chest CT Images
DOI:
https://doi.org/10.22399/ijcesen.5129Keywords:
Radiology, Pneumonia, (CNN, PCA, GLRLM )Abstract
Modern healthcare relies heavily on medical image analysis. The task of analyzing and diagnosing based solely on images is challenging, which has led to the implementation of computer-aided diagnosis techniques. RT‒PCR, a screening tool, has lower sensitivity in the diagnosis of COVID-19, and medical imaging methods such as computed tomography (CT) offer significant benefits over other methods. Segmentation is the most challenging issue when working on medical images, as the deep learning approach has recently become commonly used in diagnosis. A convolutional neural network (CNN) framework called U-Net was created for image processing's semantic pixel segmentation. This paper focuses on assisting radiologists in providing a more detailed depiction of COVID-19 infection on CT images, including various infection categories and lung conditions. In this study, we conducted an experiment on preprocessing for better segmentation results via the U-Net model. Patchwise segmentation yields better results than linear and cubic interpolation does.
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