# Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

arXiv: https://arxiv.org/abs/1705.09435 · HTML: https://mlsanity.com/paper/1705.09435/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2017.

## Built on by (most cited first, 12 of 12)

- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification](https://mlsanity.com/paper/2101.07429/) (2021)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [Segmentation of Lung Tumor from CT Images using Deep Supervision](https://mlsanity.com/paper/2111.09262/) (2021)
- [Deep Learning Applications for Lung Cancer Diagnosis: A systematic review](https://mlsanity.com/paper/2201.00227/) (2022)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer](https://mlsanity.com/paper/2207.01527/) (2022)
- [TinyML Design Contest for Life-Threatening Ventricular Arrhythmia Detection](https://mlsanity.com/paper/2305.05105/) (2023)
- [Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans](https://mlsanity.com/paper/2103.14537/) (2021)
- [Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet](https://mlsanity.com/paper/2310.03365/) (2023)
- [Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling](https://mlsanity.com/paper/2604.07329/) (2026)
- [The ecosystem of machine learning competitions: Platforms, participants, and their impact on AI development](https://mlsanity.com/paper/2604.08001/) (2026)

## Abstract (arXiv)

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.