Title:A Novel Approach to the Technique of Lung Region Segmentation Based on a
Deep Learning Model to Diagnose COVID-19 X-ray Images
Volume: 20
Author(s): Xuejie Ding, Qi Zhou, Zifan Liu, Jamal Alzobair Hammad Kowah, Lisheng Wang, Xialing Huang and Xu Liu*
Affiliation:
- College of Medicine, Guangxi University, Nanning 530004, China
Keywords:
COVID-19, Lung segmentation, Lung classification, Deep learning, FocusNet, X-ray.
Abstract:
Background:
The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray
technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films
has proven to be inefficient and time-consuming, necessitating the development of an automated classification system.
Objective:
In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray
images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis.
Methods:
We have proposed a novel deep network called “FocusNet” for precise segmentation of lung regions in chest radiographs. This segmentation
allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By
training the model on these segmented lung datasets, we sought to improve the accuracy of classification.
Results:
The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with
pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After reclassification
of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values
of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values
of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC
values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original
images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%.
Conclusion:
Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent
classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed
approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support
healthcare professionals during health crises.