PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
This addresses the problem of human error in medical diagnosis for radiologists, though it appears incremental as it builds on existing CNN methods.
The paper tackles pneumonia detection from chest X-ray images by developing PneumoXttention, an ensemble of convolutional neural networks, which achieved an F1 score of 0.82 on a test set and outperformed human radiologists on a small sample.
Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence. As part of my journey through the California Science Fair, I have developed an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. My algorithm, PneumoXttention, is an ensemble of two 13 layer convolutional neural network trained on the RSNA dataset, a dataset provided by the Radiological Society of North America, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia. The dataset was annotated by many professional radiologists in North America. It achieved an impressive F1 score, 0.82, on the test set (20% random split of RSNA dataset) and completely compensated Human Radiologists on a random set of 25 test images drawn from RSNA and NIH. I don't have a direct comparison but Stanford's Chexnet has a F1 score of 0.435 on the NIH dataset for category Pneumonia.