Journal Article

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization

Akram, Muhammad Usman, Khan, Asad, Nazir, Sajid

2023

Overview

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don’t show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git ). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique’s addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Anomalies / Artificial neural networks / Automation / Biology and Life Sciences / Chest / Classification / Computer and Information Sciences / Computer-aided medical diagnosis / Coronaviruses / COVID-19 / COVID-19 - diagnostic imaging / Datasets / Deep learning / Diagnosis / Humans / Image analysis / Image classification / Image processing / Localization / Machine learning / Medicine and Health Sciences / Methods / Neural networks / Neural Networks, Computer / Pandemics / Physical Sciences / Pneumonia / Pneumonia, Viral - diagnostic imaging / Preventable deaths / Research and Analysis Methods / Signs and symptoms / Thorax - diagnostic imaging / Transfer learning / Viral diseases / Viral pneumonia / X-Rays