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

TL;DR

This paper introduces a multi-stage deep learning framework for lung cancer detection in 3D CT scans, achieving competitive results in the Kaggle Data Science Bowl 2017 challenge.

Contribution

The paper presents a novel multi-stage deep learning approach specifically designed for lung nodule detection and malignancy classification in 3D scans.

Findings

Ranked 41st out of 1972 teams in Kaggle challenge

Effective detection and classification of lung nodules

Demonstrated advantages of multi-stage deep learning framework

Abstract

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.