Gated-dilated networks for lung nodule classification in CT scans

Mundher Al-Shabi, Hwee Kuan Lee, Maxine Tan

    Research output: Contribution to journalArticleResearchpeer-review

    6 Citations (Scopus)

    Abstract

    Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and challenging task. In this study, we propose a novel CNN architecture called Gated-Dilated (GD) networks to classify nodules as malignant or benign. Unlike previous studies, the GD network uses multiple dilated convolutions instead of max-poolings to capture the scale variations. Moreover, the GD network has a Context-Aware sub-network that analyzes the input features and guides the features to a suitable dilated convolution. We evaluated the proposed network on more than 1,000 CT scans from the LIDC-LDRI dataset. Our proposed network outperforms state-of-the-art baseline models including Multi-Crop, Resnet, and Densenet, with an AUC of >0.95. Compared to the baseline models, the GD network improves the classification accuracies of mid-range sized nodules. Furthermore, we observe a relationship between the size of the nodule and the attention signal generated by the Context-Aware sub-network, which validates our new network architecture.

    Original languageEnglish
    Pages (from-to)178827-178838
    Number of pages12
    JournalIEEE Access
    Volume7
    DOIs
    Publication statusPublished - 2019

    Keywords

    • attention network
    • computed tomography
    • convolutional neural network
    • dilated convolution
    • Lung cancer

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