Thanks to various recent prominent achievements in Computer Vision facilitated by developments in Machine Learning and Deep Learning, there emerged a trend to apply Deep Learning in diagnostic medical imaging tasks, such as Chest-Xray classification. Since most large-scale image classification benchmarks contain single-label images with a mostly balanced distribution of labels, many standard deep learning methods fail to accommodate the class imbalance and co-occurrence problems posed by the long-tailed multi-label nature of tasks like disease diagnosis such as Chest-Xray classification. Compared to conventional single-label classification problem, multi-label recognition is often more challenging due to the dominance of negative samples (when we treat multi-label classification as series of binary classification) and the long tail distribution of positive samples. In this study, we propose a new loss function called class-aware balanced loss which can solve two previous problems. We trained Swin Transformer model on Chest-Xray14 [1] dataset with our new loss and achieved higher AUC score compared to other previous works.