Unsupervised Clustering of COVID-19 Chest X-Ray Images with a Self-Organizing Feature Map
Bayley King, Siddharth Barve, Andrew J. Ford, Rashmi Jha
Abstract
Machine learning approaches are gaining popularity in the medical field for diagnostics, predictive analytics and general research. With data often being unlabeled or sparse to collect, there is a need for unsupervised learning networks in the medical field. Self-Organizing Feature Maps (SOFM) are a common application of unsupervised networks and allow for the use of unlabeled data in their training. We applied chest x-ray images of COVID-19 patients to an SOFM network and found a distinct classification between sick and healthy patients with an average euclidean distance of 1.1 between 1st and 2nd winning neurons in our testing set. We were also able to show which features of the input space had the highest weight on the classification, to study saliency of features on this unsupervised network. This work shows that unsupervised learning is able to extract features of medical data, specifically chest x-rays of COVID-19 patients, while also accurately classifying the image. This SOFM network can be found at https://github.com/king2b3/SOFM.