Objectives: This article presents a novel approach based on computer-aided diagnostic (CAD) scheme and wavelet transforms to aid pneumonia diagnosis in children, using chest radiograph images. The prototype system, named Pneumo-CAD, was designed to classify images into presence (PP) or absence of pneumonia (PA).
Materials and methods: The knowledge database for the Pneumo-CAD comprised chest images confirmed as PP or PA by two radiologists trained to interpret chest radiographs according to the WHO guidelines for the diagnosis of pneumonia in children. The performance of the Pneumo-CAD was evaluated by a subset of images randomly selected from the knowledge database. The retrieval of similar images was made by feature extraction using wavelets transform coefficients of the image. The energy of the wavelet coefficients was used to compose the feature vector in order to support the computational classification of images as PP or PA. Methodology I worked with a rank-weighted 15-nearest-neighbour scheme, while methodology II employed a distance-dependent weighting for image classification. The performance of the prototype system was assessed by the ROC curve.
Results: Overall, the Pneumo-CAD using the Haar wavelet presented the best accuracy in discriminating PP from PA for both, methodology I (AUC=0.97) and methodology II (AUC=0.94), reaching sensitivity of 100% and specificity of 80% and 90%, respectively.
Conclusion: Pneumo-CAD could represent a complementary tool to screen children with clinical suspicion of pneumonia, and so to contribute to gather information on the burden of-pneumonia estimates in order to help guide health policies toward preventive interventions.