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Purpose: The purpose of this study was to observe change in accuracies ofconvolutional neural networks (CNN) models (ratio of correct classifications tototal predictions) on thoracic radiological images by creating different binaryclassification models based on age, gender, and image pre-processing filters on14 pathologies.  Methodology: This is a quantitative research exploring variation in CNN modelaccuracies. Radiological thoracic images were divided by age and gender andpre-processed by various image processing filters.  Findings: We found partial support for enhancement to model accuracies bysegregating modeling images by age and gender and applying image processingfilters even though image processing filters are sometimes thought of asinformation filters.  Research limitations: This study may be biased because it is based onradiological images by another research that tagged the images using anautomated process that was not checked by a human.  Practical implications: Researchers may want to focus on creating modelssegregated by demographics and pre-process the modeling images using imageprocessing filters. Practitioners developing assistive technologies forthoracic diagnoses may benefit from incorporating demographics and employingmultiple models simultaneously with varying statistical likelihood.  Originality/value: This study uses demographics in model creation andutilizes 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

Convolutional neural networks model improvements using demographics and image processing filters on chest x-rays