ArticleTomography (Ann Arbor, Mich.)2024
A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.
Article in Tomography (Ann Arbor, Mich.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography.Journal of imaging informatics in medicine · 2026Article
- Development and validation of a clinical-radiomics nomogram for differentiatingFrontiers in pediatrics · 2026Article
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Authors and funding
5 authors.
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Abstract
Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.
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