A bone suppression model ensemble to improve COVID-19 detection in chest X-rays

TL;DR

This study develops an ensemble of CNN models for bone suppression in chest X-rays to enhance COVID-19 detection accuracy, demonstrating improved performance over individual models and non-bone-suppressed images.

Contribution

The paper introduces a novel ensemble approach using MS-SSIM for bone suppression in CXRs, significantly improving COVID-19 detection accuracy.

Findings

Ensemble model outperforms individual bone suppression models in MS-SSIM and other metrics.

Bone suppression enhances COVID-19 detection accuracy in chest X-rays.

Model trained on bone-suppressed images significantly outperforms on non-bone-suppressed images.

Abstract

Chest X-ray (CXR) is a widely performed radiology examination that helps to detect abnormalities in the tissues and organs in the thoracic cavity. Detecting pulmonary abnormalities like COVID-19 may become difficult due to that they are obscured by the presence of bony structures like the ribs and the clavicles, thereby resulting in screening/diagnostic misinterpretations. Automated bone suppression methods would help suppress these bony structures and increase soft tissue visibility. In this study, we propose to build an ensemble of convolutional neural network models to suppress bones in frontal CXRs, improve classification performance, and reduce interpretation errors related to COVID-19 detection. The ensemble is constructed by (i) measuring the multi-scale structural similarity index (MS-SSIM) score between the sub-blocks of the bone-suppressed image predicted by each of the top-3 performing bone-suppression models and the corresponding sub-blocks of its respective ground truth soft-tissue image, and (ii) performing a majority voting of the MS-SSIM score computed in each sub-block to identify the sub-block with the maximum MS-SSIM score and use it in constructing the final bone-suppressed image. We empirically determine the sub-block size that delivers superior bone suppression performance. It is observed that the bone suppression model ensemble outperformed the individual models in terms of MS-SSIM and other metrics. A CXR modality-specific classification model is retrained and evaluated on the non-bone-suppressed and bone-suppressed images to classify them as showing normal lungs or other COVID-19-like manifestations. We observed that the bone-suppressed model training significantly outperformed the model trained on non-bone-suppressed images toward detecting COVID-19 manifestations.