CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
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Summary
An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric.
- Type
- preprint
- Published
- 2017-11-14
- Cited by
- 3,502
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W2770241596
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:40094999
Keywords
Metric (unit), Convolutional neural network, Deep learning, Pneumonia, Computer science
References
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- Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.
- Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI
- Pneumothorax detection in chest radiographs using convolutional neural networks
- Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans
- Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
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- Abnormality Detection in Mammography using Deep Convolutional Neural Networks
- Automated Gleason grading of prostate cancer tissue microarrays via deep learning
- Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
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