  Chest X-rays are widely used to diagnose thoracic diseases, but the lack of
detailed information about these abnormalities makes it challenging to develop
accurate automated diagnosis systems, which is crucial for early detection and
effective treatment. To address this challenge, we employed deep learning
techniques to identify patterns in chest X-rays that correspond to different
diseases. We conducted experiments on the "ChestX-ray14" dataset using various
pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical
models. The best individual model was the CoAtNet, which achieved an area under
the receiver operating characteristic curve (AUROC) of 84.2%. By combining the
predictions of all trained models using a weighted average ensemble where the
weight of each model was determined using differential evolution, we further
improved the AUROC to 85.4%, outperforming other state-of-the-art methods in
this field. Our findings demonstrate the potential of deep learning techniques,
particularly ensemble deep learning, for improving the accuracy of automatic
diagnosis of thoracic diseases from chest X-rays. Code available
at:https://github.com/syednabilashraf/SynthEnsemble
