Multi-scale input mirror network for tuberculosis detection in CXR image

Computer-aided diagnosis (CAD) can detect tuberculosis (TB) cases, providing radiologists with more accurate and efficient diagnostic solutions. Various noise information in TB chest X-ray (CXR) images is a major challenge in this classification task. This study aims to propose a model with high performance in TB CXR image detection named multi-scale input mirror network(MIM-Net) based on CXR image symmetry, which consists of a multi-scale input feature extraction network and mirror loss. The multi-scale image input can enhance feature extraction, while the mirror loss can improve the network performance through self-supervision. We used a publicly available TB CXR image classification dataset to evaluate our proposed method via 5-fold cross-validation, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve (AUC) of 99.67%, 100%, 99.60%, 99.80%, 100%, and 0.999 9, respectively. Compared to other models, MIM-Net performed best in all metrics. Therefore, the proposed MIM-Net can effectively help the network learn more features and can be used to detect TB in CXR images, thus assisting doctors in diagnosing.