  Lung Cancer is the most common cause of cancer-related death worldwide. Early
and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer
Tomography (CT) chest scans can provide early treatment as well as doctor
liberation from time-consuming procedures. Deep Learning has been proven as a
popular and influential method in many medical imaging diagnosis areas. In this
study, we consider the problem of diagnostic classification between benign and
malignant lung nodules in CT images derived from a PET/CT scanner. More
specifically, we intend to develop experimental Convolutional Neural Network
(CNN) architectures and conduct experiments, by tuning their parameters, to
investigate their behavior, and to define the optimal setup for the accurate
classification. For the experiments, we utilize PET/CT images obtained from the
Laboratory of Nuclear Medicine of the University of Patras, and the publically
available database called Lung Image Database Consortium Image Collection
(LIDC-IDRI). Furthermore, we apply simple data augmentation to generate new
instances and to inspect the performance of the developed networks.
Classification accuracy of 91% and 93% on the PET/CT dataset and on a selection
of nodule images form the LIDC-IDRI dataset, is achieved accordingly. The
results demonstrate that CNNs are a trustworth method for nodule
classification. Also, the experiment confirms that data augmentation enhances
the robustness of the CNNs.
