Analysis of Pneumonia Area Development in Covid-19 Patients based on Sequential X-Ray Images Using the Seq_UB Architecture
DOI:
https://doi.org/10.48048/tis.2024.7261Keywords:
Deep learning, Segmentation, X-Ray, Pneumonia, Covid-19Abstract
Introduction
During the covid pandemic, radiologists diagnosed covid patients by analyzing chest X-ray images. The existence of a quantitative system can help visual observation of lung conditions by radiologists.
Purpose of research
This research aims to develop an image segmentation algorithm, Seq_UB, which can monitor the development of the lung condition of Covid-19 patients periodically. The Seq_UB algorithm was developed by modifying the UNet system using different CNN layers according to the UBNet v1 algorithm.
Materials and methods
The dataset for training uses the Montgomery USA Dataset, which contains 138 CXRs with a resolution of 4,020×4,892, of which there are 80 normal CXR images and 58 CXR images of patients identified with tuberculosis; the Shenzhen Hospital Dataset consists of 662 CXR images, of which there are 336 abnormal CXR images.
Result and discussion
The results show that reducing the input image size does not significantly affect the accuracy of the segmentation results. However, reducing the input image size will affect the resolution of the segmentation results. Where the smaller the input image size, the lower the resolution obtained. This will have an impact on the final interpretation of the segmentation results. Research shows that an input image size of 512 is the best because the resolution of the segmentation results is still very accurate. This study shows that the Seq_UB architecture can perform X-ray image segmentation with relatively stable accuracy and lower computational burden. An interesting pattern was found, where Covid-19 patients quantitatively experienced fluctuations in image segmentation size.
Conclusions
The Seq_UB system can perform well with a segmentation accuracy of 96 %, and the processing speed takes 0.91 s. A desktop GUI was designed to segment X-ray images more effectively.
HIGHLIGHTS
- Development of the Seq_UB algorithm: This research presents the development of an X-ray image segmentation algorithm with a computational workload that is faster, more accurate, and lighter
- Quantitative estimation of the lung area percentage of Covid-19 patients based on segmentation with the Seg_UB model
- Monitoring the development of lung conditions of Covid-19 patients quantitatively by comparing it with normal CXR using the Seg_UB model
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