A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

Abstract

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection's severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Figure 1

Block diagram of the proposed architecture. Stage 1 involves lung segmentation, Stage 2 focuses on disease classification, and Stage 3 incorporates infection segmentation and severity assessment.

Figure 2

Mathematical example of the proposed ensemble approach. Initially, rank scores are computed from the three base CNNs. These scores are then fused together to generate an overall score, determining the final classification outcome.

Figure 3

The outcome of the infection segmentation and severity assessment for COVID-19 samples. Blue areas are actual lung regions, and red areas are infection regions predicted by the model..