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

• Published in: IEEE journal of biomedical and health informatics Vol. 23; no. 3; pp. 960 - 968
• Main Authors: Sahu, Pranjal, Yu, Dantong, Dasari, Mallesham, Hou, Fei, Qin, Hong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Architecture; Artificial intelligence; Cancer; Classification; Computational modeling; Computed tomography; Consortia; Cross-sections; Deep learning; Electronic devices; Feature extraction; Humans; Image databases; Imaging, Three-Dimensional - methods; Lightweight; Lung; Lung - diagnostic imaging; Lung cancer; Lung Neoplasms - classification; Lung Neoplasms - diagnostic imaging; Malignancy; Medical imaging; Neural Networks, Computer; nodule classification; ROC Curve; Solitary Pulmonary Nodule - classification; Solitary Pulmonary Nodule - diagnostic imaging; spherical sampling; Task analysis; Three dimensional models; Three-dimensional displays; Tomography, X-Ray Computed - methods; Training; transfer Learning; Weight reduction
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2018.2879834

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 that proposed computationally intensive deep ensemble models or three-dimensional 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 the nodule's spatial annotation and works directly on the cross sections generated from volume enclosing the nodule. We evaluated the proposed method on lung image database consortium (LIDC) and image database resource initiative (IDRI) dataset. It achieved the 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 the nodule's malignancy that facilitates in the interpretation of results. Because of being lightweight, the model could be ported to mobile devices, which brings the power of artificial intelligence (AI) driven application directly into the practitioner's hand.