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CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

ChexNet is a 121 layer convolutional neural network developed at Stanford University which reads chest X-ray images to diagnose 14 different chest diseases including Pneumonia.In this study 112,120 chest X-ray images of 30,805 patients were analyzed to develop the algorithm.CheXNet achieved an F1 score of 0.435 (95% CI 0.387, 0.481), higher than the radiologist average of 0.387 (95% CI 0.330, 0.442).

Read this paper using the link below:

What is an F-1 score?

F1 score is harmonic mean of precision and recall used frequently in machine learning to measure accuracy of a test.

F1 = 2 x (Precision x Recall)/(Precision + Recall)

Best F1 score is 1 and worst is 0.