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

• URL: http://arxiv.org/abs/2504.12249v3
• Date: Mon, 21 Jul 2025 15:57:00 GMT
• Title: Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography
• Authors: Zhijin He, Alan B. McMillan,
• Abstract summary: We evaluate radiomics-based and deep learning-based approaches for disease detection in chest radiography.<n>Deep learning models learn directly from image data, while radiomics-based models extract handcrafted features.<n>These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI.
• 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 and vision transformers, learn directly from image data, radiomics-based models extract handcrafted features, offering potential advantages in data-limited scenarios. We systematically compared the diagnostic performance of various AI models, including Decision Trees, Gradient Boosting, Random Forests, Support Vector Machines, and Multi-Layer Perceptrons for radiomics, against state-of-the-art deep learning models such as InceptionV3, EfficientNetL, and ConvNeXtXLarge. Performance was evaluated across multiple sample sizes. At 24 samples, EfficientNetL achieved an AUC of 0.839, outperforming SVM (AUC = 0.762). At 4000 samples, InceptionV3 achieved the highest AUC of 0.996, compared to 0.885 for Random Forest. A Scheirer-Ray-Hare test confirmed significant main and interaction effects of model type and sample size on all metrics. Post hoc Mann-Whitney U tests with Bonferroni correction further revealed consistent performance advantages for deep learning models across most conditions. These findings provide statistically validated, data-driven recommendations for model selection in diagnostic AI. Deep learning models demonstrated higher performance and better scalability with increasing data availability, while radiomics-based models may remain useful in low-data contexts. This study addresses a critical gap in AI-based diagnostic research by offering practical guidance for deploying AI models across diverse clinical environments.

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