# Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach

arXiv: https://arxiv.org/abs/1808.10858 · HTML: https://mlsanity.com/paper/1808.10858/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

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

- [Explainable artificial intelligence (XAI) in deep learning-based medical image analysis](https://mlsanity.com/paper/2107.10912/) (2021)
- [Semi-supervised learning for medical image classification using imbalanced training data](https://mlsanity.com/paper/2108.08956/) (2021)
- [LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model](https://mlsanity.com/paper/2304.04814/) (2023)
- [A clinical validation of VinDr-CXR, an AI system for detecting abnormal chest radiographs](https://mlsanity.com/paper/2104.02256/) (2021)
- [Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey](https://mlsanity.com/paper/2107.14037/) (2021)
- [Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier](https://mlsanity.com/paper/2410.12584/) (2024)
- [Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection](https://mlsanity.com/paper/2201.09563/) (2022)
- [Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network](https://mlsanity.com/paper/2207.03050/) (2022)
- [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)
- [3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI](https://mlsanity.com/paper/2103.13497/) (2021)
- [CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19](https://mlsanity.com/paper/2106.05861/) (2021)
- [Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography](https://mlsanity.com/paper/2607.05628/) (2026)

## Abstract (arXiv)

Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme was explored as a means to classify lung cancer using chest X-ray images. The model was trained on a lung nodules dataset before training on the lung cancer dataset to alleviate the problem of a small dataset. The proposed model yields 74.43±6.01% of mean accuracy, 74.96±9.85% of mean specificity, and 74.68±15.33% of mean sensitivity. The proposed model also provides a heatmap for identifying the location of the lung nodule. These findings are promising for further development of chest x-ray-based lung cancer diagnosis using the deep learning approach. Moreover, these findings solve the problem of small dataset.