Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using
  hybrid CNN-Transformer Architecture
Sonit Singh
Medical imaging has been used for diagnosis of various conditions, making it
one of the most powerful resources for effective patient care. Due to
widespread availability, low cost, and low radiation, chest X-ray is one of the
most sought after radiology examination for the diagnosis of various thoracic
diseases. Due to advancements in medical imaging technologies and increasing
patient load, current radiology workflow faces various challenges including
increasing backlogs, working long hours, and increase in diagnostic errors. An
automated computer-aided diagnosis system that can interpret chest X-rays to
augment radiologists by providing actionable insights has potential to provide
second opinion to radiologists, highlight relevant regions in the image, in
turn expediting clinical workflow, reducing diagnostic errors, and improving
patient care. In this study, we applied a novel architecture augmenting the
DenseNet121 Convolutional Neural Network (CNN) with multi-head self-attention
mechanism using transformer, namely SA-DenseNet121, that can identify multiple
thoracic diseases in chest X-rays. We conducted experiments on four of the
largest chest X-ray datasets, namely, ChestX-ray14, CheXpert, MIMIC-CXR-JPG,
and IU-CXR. Experimental results in terms of area under the receiver operating
characteristics (AUC-ROC) shows that augmenting CNN with self-attention has
potential in diagnosing different thoracic diseases from chest X-rays. The
proposed methodology has the potential to support the reading workflow, improve
efficiency, and reduce diagnostic errors.