CheXNet is a deep learning algorithm that detects pneumonia from chest X-rays with performance exceeding that of practicing radiologists. The algorithm is a 121-layer convolutional neural network (CNN) trained on the ChestX-ray14 dataset, which contains over 100,000 frontal-view X-ray images with 14 different thoracic diseases. The model was tested against four practicing radiologists, and it achieved a higher F1 score (0.435) than the average radiologist performance (0.387). CheXNet was extended to detect all 14 diseases in the dataset and outperformed previous state-of-the-art results on all 14 diseases. CheXNet uses dense connections and batch normalization to optimize the deep network. It is trained on the ChestX-ray14 dataset, which includes 112,120 images of 30,805 unique patients. The model was evaluated on a test set of 420 images, with annotations provided by four radiologists. The model's performance was statistically significantly higher than that of the radiologists, with a difference in F1 scores of 0.051 (95% CI 0.005, 0.084). CheXNet also provides class activation maps (CAMs) to visualize the areas of the image most indicative of the disease. The model was tested on various thoracic pathologies, including pneumonia, and achieved state-of-the-art results on all 14 classes. The model's performance was compared to previous state-of-the-art results, and it outperformed them on several classes, including Mass, Nodule, Pneumonia, and Emphysema. CheXNet has the potential to improve healthcare delivery by providing an automated diagnosis tool that can detect pneumonia and other thoracic pathologies at the level of expert radiologists. This technology could be particularly valuable in regions with limited access to skilled radiologists. The model's ability to detect multiple diseases and provide visualizations of the most relevant areas of the image makes it a promising tool for clinical use.CheXNet is a deep learning algorithm that detects pneumonia from chest X-rays with performance exceeding that of practicing radiologists. The algorithm is a 121-layer convolutional neural network (CNN) trained on the ChestX-ray14 dataset, which contains over 100,000 frontal-view X-ray images with 14 different thoracic diseases. The model was tested against four practicing radiologists, and it achieved a higher F1 score (0.435) than the average radiologist performance (0.387). CheXNet was extended to detect all 14 diseases in the dataset and outperformed previous state-of-the-art results on all 14 diseases. CheXNet uses dense connections and batch normalization to optimize the deep network. It is trained on the ChestX-ray14 dataset, which includes 112,120 images of 30,805 unique patients. The model was evaluated on a test set of 420 images, with annotations provided by four radiologists. The model's performance was statistically significantly higher than that of the radiologists, with a difference in F1 scores of 0.051 (95% CI 0.005, 0.084). CheXNet also provides class activation maps (CAMs) to visualize the areas of the image most indicative of the disease. The model was tested on various thoracic pathologies, including pneumonia, and achieved state-of-the-art results on all 14 classes. The model's performance was compared to previous state-of-the-art results, and it outperformed them on several classes, including Mass, Nodule, Pneumonia, and Emphysema. CheXNet has the potential to improve healthcare delivery by providing an automated diagnosis tool that can detect pneumonia and other thoracic pathologies at the level of expert radiologists. This technology could be particularly valuable in regions with limited access to skilled radiologists. The model's ability to detect multiple diseases and provide visualizations of the most relevant areas of the image makes it a promising tool for clinical use.