  Lung cancer classification in screening computed tomography (CT) scans is one
of the most crucial tasks for early detection of this disease. Many lives can
be saved if we are able to accurately classify malignant/cancerous lung
nodules. Consequently, several deep learning based models have been proposed
recently to classify lung nodules as malignant or benign. Nevertheless, the
large variation in the size and heterogeneous appearance of the nodules makes
this task an extremely challenging one. We propose a new Progressive Growing
Channel Attentive Non-Local (ProCAN) network for lung nodule classification.
The proposed method addresses this challenge from three different aspects.
First, we enrich the Non-Local network by adding channel-wise attention
capability to it. Second, we apply Curriculum Learning principles, whereby we
first train our model on easy examples before hard ones. Third, as the
classification task gets harder during the Curriculum learning, our model is
progressively grown to increase its capability of handling the task at hand. We
examined our proposed method on two different public datasets and compared its
performance with state-of-the-art methods in the literature. The results show
that the ProCAN model outperforms state-of-the-art methods and achieves an AUC
of 98.05% and an accuracy of 95.28% on the LIDC-IDRI dataset. Moreover, we
conducted extensive ablation studies to analyze the contribution and effects of
each new component of our proposed method.
