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

Document Type

Article

Publication Date

5-1-2019

Abstract

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.

Identifier

85056337487 (Scopus)

Publication Title

IEEE Journal of Biomedical and Health Informatics

External Full Text Location

https://doi.org/10.1109/JBHI.2018.2879834

e-ISSN

21682208

ISSN

21682194

PubMed ID

30418891

First Page

960

Last Page

968

Issue

3

Volume

23

Grant

NSF IIS-1715985

Fund Ref

National Science Foundation

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