Neural Networks for Lung Cancer Detection through Radiomic Features

Lung cancer is one of primary premature death causes. As a matter of fact, people die more for lung cancer than prostate, colon or breast. To assist medics and radiologists in the diagnosis formulation, in this paper we propose a neural network-based method aimed to discriminate between different lung cancer types. We exploit a set of 30 radiomic feature directly obtained from magnetic resonance, tuning the neural network model when the momentum and the loss functions are varying with the aim to find the best model in terms of features and network parameters. We evaluate the effectiveness of the proposed method on a dataset of 2000 MRI labelled through medical reports, obtaining a precision equal to 0.918 and a recall equal to 0.923 in T1a lung cancer detection while a precision equal to 0.931 and a recall equal to 0.918 is obtained in T2b lung cancer detection.