Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography

• URL: http://arxiv.org/abs/2504.12249v1
• Date: Wed, 16 Apr 2025 16:54:37 GMT
• Title: Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
• Authors: Zhijin He, Alan B. McMillan,
• Abstract summary: This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>It focuses on COVID-19, lung opacity, and viral pneumonia.<n>The results aim to inform the integration of AI-driven diagnostic tools in clinical practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The application of artificial intelligence (AI) in medical imaging has revolutionized diagnostic practices, enabling advanced analysis and interpretation of radiological data. This study presents a comprehensive evaluation of radiomics-based and deep learning-based approaches for disease detection in chest radiography, focusing on COVID-19, lung opacity, and viral pneumonia. While deep learning models, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), learn directly from image data, radiomics-based models extract and analyze quantitative features, potentially providing advantages in data-limited scenarios. This study systematically compares the diagnostic accuracy and robustness of various AI models, including Decision Trees, Gradient Boosting, Random Forests, Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP) for radiomics, against state-of-the-art computer vision deep learning architectures. Performance metrics across varying sample sizes reveal insights into each model's efficacy, highlighting the contexts in which specific AI approaches may offer enhanced diagnostic capabilities. The results aim to inform the integration of AI-driven diagnostic tools in clinical practice, particularly in automated and high-throughput environments where timely, reliable diagnosis is critical. This comparative study addresses an essential gap, establishing guidance for the selection of AI models based on clinical and operational needs.

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