An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection

TL;DR

This paper introduces XCCNet, an explainable deep learning model combining contrastive transformers and dilated convolutions, designed to improve pediatric pneumonia detection from chest X-rays, especially addressing low-quality images and data imbalance.

Contribution

The novel XCCNet model integrates contrastive transformers with dilated convolutions and explainability, advancing pediatric pneumonia detection accuracy and interpretability over existing methods.

Findings

XCCNet outperforms state-of-the-art models on four datasets.

Effective handling of low-quality radiographs and data imbalance.

Provides explainability through feature visualization aligned with attention regions.

Abstract

Pediatric pneumonia remains a significant global threat, posing a larger mortality risk than any other communicable disease. According to UNICEF, it is a leading cause of mortality in children under five and requires prompt diagnosis. Early diagnosis using chest radiographs is the prevalent standard, but limitations include low radiation levels in unprocessed images and data imbalance issues. This necessitates the development of efficient, computer-aided diagnosis techniques. To this end, we propose a novel EXplainable Contrastive-based Dilated Convolutional Network with Transformer (XCCNet) for pediatric pneumonia detection. XCCNet harnesses the spatial power of dilated convolutions and the global insights from contrastive-based transformers for effective feature refinement. A robust chest X-ray processing module tackles low-intensity radiographs, while adversarial-based data augmentation mitigates the skewed distribution of chest X-rays in the dataset. Furthermore, we actively integrate an explainability approach through feature visualization, directly aligning it with the attention region that pinpoints the presence of pneumonia or normality in radiographs. The efficacy of XCCNet is comprehensively assessed on four publicly available datasets. Extensive performance evaluation demonstrates the superiority of XCCNet compared to state-of-the-art methods.