Segmentation of Chest X-Ray Images Using U-Net Model

被引：0

作者：

Hashem S.A. ^{[1
]}

Kamil M.Y. ^{[1
]}

机构：

[1] College of Science, Mustansiriyah University, Baghdad

来源：

Mendel | 2022年 / 28卷 / 02期

关键词：

CNN; Coronavirus; Deep learning; Lung; Segmentation; U-Net; X-ray;

D O I：

10.13164/mendel.2022.2.049

中图分类号：

学科分类号：

摘要：

Medical imaging, such as chest X-rays, gives an acceptable image of lung functions. Manipulating these images by a radiologist is difficult, thus delaying the diagnosis. Coronavirus is a disease that affects the lung area. Lung segmentation has a significant function in assessing lung disorders. The process of segmentation has seen the widespread use of deep learning algorithms. The U-Net is one of the most significant semantic segmentation frameworks for a convolutional neural network. In this paper, the proposed U-Net architecture is evaluated on datasets of 565 X-ray images, divided into 500 training images and 65 validation images. The findings of the experiments demonstrated that the suggested strategy successfully achieved competitive outcomes with 91.47% and 89.18% accuracy, 0.7494 and 0.7480 IoU, 19.23% and 26.11% loss for training and validation images, respectively. DR. © 2022, Brno University of Technology. All rights reserved.

引用

页码：49 / 53

页数：4

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