Image classification of pediatric pneumonia : A comparative study of supervised statistical learning techniques

Sammanfattning

A child dies of pneumonia every 39 seconds, and the process of preventing deaths caused by pneumonia has been considerably slower compared to other infectious diseases. Meanwhile, the traditional method of manually diagnosing patients has reached its ceiling on performance. With the support of a machine learning classification algorithm to help with the screening of pneumonia from x-ray images combined with the expertise of a physician, the identification and diagnosis of pediatric pneumonia should be both quicker and more accurate. In this study, four different types of supervised machine learning algorithms have been trained, tested, and evaluated to see which model could predict most accurately whether a patient in an x-ray image has pneumonia or not. The four models included in this study have been trained by four different supervised machine learning algorithms: logistic regression, k-nearest-neighbor, support vector machine, and neural network. The results show that KNN has the highest sensitivity, NN adapts to new data the best by not being under- or overfit. SVM had the highest balanced accuracy on both train and test data but a proportionally high difference between the in- and out-sample error. In conclusion, relatively high performance can be achieved when classifying x-ray images of pneumonia even with limited resources.