A Lightweight Multi-section CNN for Lung Nodule Classification and Malignancy Estimation.

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's structural information from CT scans in a Computer-aided system is a challenging task. Unlike previous models which proposed computationally intensive deep ensemble models or 3D CNN models, we propose a lightweight, multiple view sampling based Multi-section CNN architecture. The model obtains a nodule's cross-sections from multiple view angles and encodes the nodule's volumetric information into a compact representation by aggregating information from its different cross-sections via a view pooling layer. The compact feature is subsequently used for the task of nodule classification. The method does not require nodule's spatial annotation and works directly on the crosssections generated from volume enclosing the nodule. We evaluated the proposed method on LIDC-IDRI dataset. It achieved state-of-the-art performance with a mean 93.18% classification accuracy. The architecture could also be used to select the representative cross-sections determining nodule's malignancy which facilitates in the interpretation of results. Because of being lightweight the model could be ported to mobile devices which brings the power of AI driven application directly into practitioner's hand.