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The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of multiple diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan at the same time, i.e. multiple labels. Existing literature uses state-of-the-art deep learning models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple disease labels in each image. However, most of them don’t consider the label relationship explicitly between present and absent classes. In this work we have proposed a pair of novel error functions that can be employed for any deep learning model, Multi-label Softmax Loss (MSML) and Correlation Loss (CorLoss), to specifically address the properties of multiple labels and visually similar data. Moreover, we …