# Deep Learning Applications for Lung Cancer Diagnosis: A systematic review

arXiv: https://arxiv.org/abs/2201.00227 · HTML: https://mlsanity.com/paper/2201.00227/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge](https://mlsanity.com/paper/1612.08012/) (2016)
- [A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans](https://mlsanity.com/paper/1902.03233/) (2019)
- [Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge](https://mlsanity.com/paper/1705.09435/) (2017)
- [Towards automatic pulmonary nodule management in lung cancer screening with deep learning](https://mlsanity.com/paper/1610.09157/) (2016)
- [Knowledge-based Analysis for Mortality Prediction from CT Images](https://mlsanity.com/paper/1902.07687/) (2019)
- [Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer](https://mlsanity.com/paper/2104.14420/) (2021)

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

- [Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)](https://mlsanity.com/paper/2408.15355/) (2024)
- [DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images](https://mlsanity.com/paper/2505.09334/) (2025)

## Abstract (arXiv)

Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researchers in this field. The main purpose of this work is to identify the challenges that exist in lung cancer based on deep learning. The survey is systematically written that combines regular mapping and literature review to review 32 conference and journal articles in the field from 2016 to 2021. After analysing and reviewing the articles, the questions raised in the articles are being answered. This research is superior to other review articles in this field due to the complete review of relevant articles and systematic write up.