AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Abstract

Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.