Automated lung cancer diagnosis from chest X-ray images using convolutional neural networks.

ABSTRACT:

Background/objectives

Lung cancer is the leading cause of cancer-related deaths worldwide. While computed tomography (CT) scans provide more comprehensive medical information than chest X-rays (CXR), the high cost and limited availability of CT technology in rural areas pose significant challenges. CXR images, however, could serve as a potential preliminary diagnostic tool in diagnosing lung cancer, especially when combined with a computer-aided diagnosis (CAD) system. This study aims to enhance the accuracy and accessibility of lung cancer detection using a custom-designed convolutional neural network (CNN) trained on CXR images.

Methods

A custom-designed CNN was trained on an openly accessible CXR dataset from the Japanese Society for Radiological Technology (JSRT). Prior

SUBMITTER: Aboelghiet A 

PROVIDER: S-EPMC12453845 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature