  Radiology reports are an important means of communication between
radiologists and other physicians. These reports express a radiologist's
interpretation of a medical imaging examination and are critical in
establishing a diagnosis and formulating a treatment plan. In this paper, we
propose a Bi-directional convolutional neural network (Bi-CNN) model for the
interpretation and classification of mammograms based on breast density and
chest radiographic radiology reports based on the basis of chest pathology. The
proposed approach helps to organize databases of radiology reports, retrieve
them expeditiously, and evaluate the radiology report that could be used in an
auditing system to decrease incorrect diagnoses. Our study revealed that the
proposed Bi-CNN outperforms the random forest and the support vector machine
methods.
