Papers › Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification

Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification

10 May 2025arXiv:2505.06646archive 2025-07-28

Daniel Strick, Carlos Garcia, Anthony Huang

Deep learning for radiologic image analysis is a rapidly growing field in biomedical research and is likely to become a standard practice in modern medicine. On the publicly available NIH ChestX-ray14 dataset, containing X-ray images that are classified by the presence or absence of 14 different diseases, we reproduced an algorithm known as CheXNet, as well as explored other algorithms that outperform CheXNet's baseline metrics. Model performance was primarily evaluated using the F1 score and AUC-ROC, both of which are critical metrics for imbalanced, multi-label classification tasks in medical imaging. The best model achieved an average AUC-ROC score of 0.85 and an average F1 score of 0.39 across all 14 disease classifications present in the dataset.

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Code

dstrick17/Deep-Learning-Project officialpytorchMIT report

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Tasks

Multi-Label Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multi-Label Classification	ChestX-ray14	Improved CheXNet (DannyNet, dstrick17 et al., 2025)	Average AUC on 14 label	85.266	#2 of 4	Archive leaderboard	report
Multi-Label Classification	ChestX-ray14	Improved CheXNet (DannyNet, dstrick17 et al., 2025)	Macro F1	0.38605	#2 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization CheXNet Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Softmax

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