# DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification

arXiv: https://arxiv.org/abs/1709.05538 · HTML: https://mlsanity.com/paper/1709.05538/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels](https://mlsanity.com/paper/2203.07725/) (2022)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification](https://mlsanity.com/paper/2108.05386/) (2021)
- [Explainable AI Technique in Lung Cancer Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2508.10196/) (2025)
- [Deep Neural Networks with Ordinal Loss for Medical Applications](https://mlsanity.com/paper/2606.25769/) (2026)

## Abstract (arXiv)

In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network (DPN) features is proposed. The nodule classification subnetwork is validated on a public dataset from LIDC-IDRI, on which it achieves better performance than state-of-the-art approaches, and surpasses the average performance of four experienced doctors. For the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate the DeepLung is comparable to the experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.