Evaluating Diagnostic Thresholds for Pneumonia Severity Using HOG Features and Deep Learning Models

Abstract

Pneumonia severity diagnosis is vital for effective and efficient patient treatment and management. This study evaluates the efficacy of different diagnostic thresholds for pneumonia severity using Histogram of Oriented Gradients (HOG) features and deep learning models on chest X-ray images. Utilizing the Kaggle chest X-ray dataset, HOG feature extraction was applied, followed by Pearson correlation to compare a randomly selected X-ray with pneumonia images. Three diagnostic thresholds were used for severity diagnosis. The results demonstrated that all thresholds performed well in detecting moderate cases, with Threshold1 and Threshold2 both achieving an accuracy of 89% and an F1-score of 0.94. Threshold3 had an accuracy of 66% and F1-scores of 0.62 and 0.94 in identifying mild and moderate cases, respectively. The evaluation metrics included accuracy, precision, recall, and F1-score. These findings suggest that careful selection of diagnostic thresholds can significantly impact the accuracy and reliability of pneumonia severity diagnosis using deep learning models, with potential applications in clinical settings.