# Gated-Dilated Networks for Lung Nodule Classification in CT scans

arXiv: https://arxiv.org/abs/1901.00120 · HTML: https://mlsanity.com/paper/1901.00120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models](https://mlsanity.com/paper/2303.11177/) (2023)
- [3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis](https://mlsanity.com/paper/2207.13830/) (2022)

## Abstract (arXiv)

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.