  Purpose: The purpose of this study was to observe change in accuracies of
convolutional neural networks (CNN) models (ratio of correct classifications to
total predictions) on thoracic radiological images by creating different binary
classification models based on age, gender, and image pre-processing filters on
14 pathologies.
  Methodology: This is a quantitative research exploring variation in CNN model
accuracies. Radiological thoracic images were divided by age and gender and
pre-processed by various image processing filters.
  Findings: We found partial support for enhancement to model accuracies by
segregating modeling images by age and gender and applying image processing
filters even though image processing filters are sometimes thought of as
information filters.
  Research limitations: This study may be biased because it is based on
radiological images by another research that tagged the images using an
automated process that was not checked by a human.
  Practical implications: Researchers may want to focus on creating models
segregated by demographics and pre-process the modeling images using image
processing filters. Practitioners developing assistive technologies for
thoracic diagnoses may benefit from incorporating demographics and employing
multiple models simultaneously with varying statistical likelihood.
  Originality/value: This study uses demographics in model creation and
utilizes image processing filters to improve model performance.
  Keywords: Convolutional Neural Network (CNN), Chest X-Ray, ChestX-ray14,
Lung, Atelectasis, Cardiomegaly, Consolidation, Edema, Effusion, Emphysema,
Infiltration, Mass, Nodule, Pleural Thickening, Pneumonia, Pneumathorax
