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

TL;DR

This study investigates how incorporating demographic data and applying image processing filters can improve CNN model accuracy in classifying thoracic radiological images, with potential benefits for medical diagnostics.

Contribution

It introduces a novel approach of using demographics and image filters to enhance CNN performance on chest X-ray classification tasks.

Findings

Segregating images by age and gender can improve model accuracy.

Applying image processing filters sometimes enhances classification performance.

Demographic-based modeling and image pre-processing are beneficial for thoracic image analysis.

Abstract

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