English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Radiology reports are an important means of communication betweenradiologists and other physicians. These reports express a radiologist'sinterpretation of a medical imaging examination and are critical inestablishing a diagnosis and formulating a treatment plan. In this paper, wepropose a Bi-directional convolutional neural network (Bi-CNN) model for theinterpretation and classification of mammograms based on breast density andchest radiographic radiology reports based on the basis of chest pathology. Theproposed approach helps to organize databases of radiology reports, retrievethem expeditiously, and evaluate the radiology report that could be used in anauditing system to decrease incorrect diagnoses. Our study revealed that theproposed Bi-CNN outperforms the random forest and the support vector machinemethods.

Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results