Local and global transformations to improve learning of medical images applied to chest radiographs

A potential drawback of computer-aided diagnosis (CAD) systems is that they tend to capture the noise characteristics along with signal variations due to a limited number of sources used in training. This leads to a decrease in performance on data from different sources. The variations in scanner settings, device manufacturers and sites pose a significant challenge to the learning capabilities of the CAD systems like chest radiographs, also called Chest X-rays (CXR). In the proposed work, we investigate if preprocessing transformations like global normalization along with local enhancements are good to tackle the variability of data from multiple sources on a supervised CXR classification system. We also propose a detail enhancement filter to enhance both finer structures and opacities in CXRs. With the proposed preprocessing improvement, experiments were performed on 13,000 images across 3 public and one private data source using Dense Convolutional Network (DenseNet). The sensitivity at equal error rate (mean ± sd) improved from 0.888 ± 0.043 to 0.931 ± 0.030 by applying a combination of global histogram equalization with the proposed detail enhancement filter when compared to the raw images. We conclude that the proposed transformations are effective in improving the learning of CXRs from different data sources.