Machine Learning For The Diagnosis Of Lung Disease

This thesis presents two machine learning based diagnosis classifiers for lung cancer and interstitial lung disease (ILD). The first classifier is trained on a pathology confirmed lung nodule dataset and is capable of differentiating between primary and secondary cancer. The second classifier is capable of creating a differential diagnosis for ILD. Both classifiers are designed to improve diagnosis of these diseases and are intended for use in the clinical setting.