A Convolutional Attention Mapping Deep Neural Network For Classification And Localization Of Cardiomegaly On Chest X-rays

This article presents a convolutional attention mapping deep learning model, Cardio-XAttentionNet, to classify and localize cardiomegaly effectively. The model is trained on ChestX-Ray14, a publicly accessible chest X-ray dataset. The best single model achieves an overall precision, recall, F-1 measure and area under curve (AUC) scores of 0.87, 0.85, 0.86 and 0.89, respectively, for the classification of the cardiomegaly. The results demonstrate that the Cardio-XAttentionNet model captures the cardiomegaly class information at image-level as well as localization at pixel-level on chest x-rays. A comparative analysis between the proposed model and existing GAP based models shows that the proposed model achieves a state-of-the-art performance on this dataset for cardiomegaly detection.