# Deep Learning Approach for Early Stage Lung Cancer Detection

arXiv: https://arxiv.org/abs/2302.02456 · HTML: https://mlsanity.com/paper/2302.02456/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification](https://mlsanity.com/paper/1806.00712/) (2018)
- [Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs](https://mlsanity.com/paper/2010.11682/) (2020)

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

- [Tackling Heterogeneity in Medical Federated learning via Vision Transformers](https://mlsanity.com/paper/2310.09444/) (2023)
- [Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging](https://mlsanity.com/paper/2507.09731/) (2025)

## Abstract (arXiv)

Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung cancer patients is very low compared to other cancer patients due to late diagnostics. Thus, early lung cancer diagnostics is crucial for patients to receive early treatments, increasing the survival rate or even becoming cancer-free. This paper proposed a deep-learning model for early lung cancer prediction and diagnosis from Computed Tomography (CT) scans. The proposed mode achieves high accuracy. In addition, it can be a beneficial tool to support radiologists' decisions in predicting and detecting lung cancer and its stage.