ArticleScientific reports2020
Comparing different deep learning architectures for classification of chest radiographs.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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59 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis?IEEE access : practical innovations, open solutions · 2025Article
- Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.JMIR formative research · 2024Article
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- Deep learning enabled label-free microfluidic droplet classification for single cell functional assays.Frontiers in bioengineering and biotechnology · 2024Article
- Multiparametric MRI-based radiomics approach with deep transfer learning for preoperative prediction of Ki-67 status in sinonasal squamous cell carcinoma.Frontiers in oncology · 2024Article
- Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.Scientific reports · 2023Article
- Convolutional Neural Networks (CNNs) for Pneumonia Classification on Pediatric Chest Radiographs.Cureus · 2023Article
- Effect of image resolution on automated classification of chest X-rays.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data.Scientific reports · 2023Article
- Hybrid Classical-Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays.Journal of imaging · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.
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