Abstract
Fast and accurate diagnosis is critical for the triage and management of pneumonia, particularly in the current scenario of a COVID-19 pandemic, where this pathology is a major symptom of the infection. With the objective of providing tools for that purpose, this study assesses the potential of three textural image characterisation methods: radiomics, fractal dimension and the recently developed superpixel-based histon, as biomarkers to be used for training Artificial Intelligence (AI) models in order to detect pneumonia in chest X-ray images. Models generated from three different AI algorithms have been studied: K-Nearest Neighbors, Support Vector Machine and Random Forest. Two open-access image datasets were used in this study. In the first one, a dataset composed of paediatric chest X-ray, the best performing generated models achieved an 83.3% accuracy with 89% sensitivity for radiomics, 89.9% accuracy with 93.6% sensitivity for fractal dimension and 91.3% accuracy with 90.5% sensitivity for superpixels based histon. Second, a dataset derived from an image repository developed primarily as a tool for studying COVID-19 was used. For this dataset, the best performing generated models resulted in a 95.3% accuracy with 99.2% sensitivity for radiomics, 99% accuracy with 100% sensitivity for fractal dimension and 99% accuracy with 98.6% sensitivity for superpixel-based histons. The results confirm the validity of the tested methods as reliable and easy-to-implement automatic diagnostic tools for pneumonia.
Keywords: COVID-19; Chest imaging; Diagnostic imaging; Fractal dimension; Histon; Pneumonia; Radiomics; Superpixels; X-ray.
【저자키워드】 COVID-19, Pneumonia, Radiomics, X-ray, Diagnostic imaging, Fractal dimension, chest imaging, Histon, Superpixels, 【초록키워드】 pathology, Biomarker, Biomarkers, COVID-19 pandemic, artificial intelligence, Infection, Triage, diagnostic, Diagnosis, Symptom, X-ray, sensitivity, Accuracy, artificial, Diagnostic imaging, management, Algorithm, Model, Validity, Random forest, chest X-ray, dataset, support vector machine, Critical, Fractal dimension, chest imaging, Chest X-ray images, Repository, characterisation, second, forest, intelligence, Fractal, tested, was used, detect, composed, were used, 【제목키워드】 Pneumonia, Chest X-ray image, feature, Automatic,