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Hodhod and Colleagues Use Deep Learning to Diagnose Pneumonia

A new study by TSYS School computer science professor Rania Hodhod and research colleagues from Princess Nourah Bint Abdulrahman University (Saudi Arabia) and Ain Shams University (Egypt) extends the academic literature demonstrating how artificial intelligence, in particular deep learning, has proven to be efficient in medical diagnosis.  The study, which appears in the current issue of Electronics, introduces a new hybrid deep learning model for pneumonia diagnosis based on chest CT scans.  The core of the model developed by Hodhod and colleagues includes an expectation-maximization algorithm that works to extract the regions of interest from the chest CT scans while also preventing it from learning trivial solutions.  To test their model, the researchers examined a dataset of chest X-rays for pneumonia from the Kaggle website.  The data set contains 5,856 images with 1,583 normal cases and 4,273 pneumonia cases, with an imbalance ratio of 0.46.  To better balance the data, Hodhod and colleagues implemented several operations including zooming, flipping, shifting and rotation, ultimately resulting in an imbalance ratio of only 0.028.  The computational analysis of the results show that the proposed model is quite promising, as it provides an average accuracy value of 98.6 percent.  

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