PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention

TL;DR

PneumoXttention is an ensemble CNN that detects pneumonia in chest X-rays with high accuracy, outperforming some existing models and aiding radiologists by reducing human error.

Contribution

The paper introduces PneumoXttention, a novel ensemble CNN model trained on a large annotated dataset, significantly improving pneumonia detection in chest X-rays.

Findings

Achieved an F1 score of 0.82 on the RSNA dataset.

Successfully compensated human radiologists in test cases.

Outperformed Stanford's Chexnet in pneumonia detection accuracy.

Abstract

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.