Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer. Academic Article uri icon

Overview

abstract

  • Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.

publication date

  • October 1, 2017

Research

keywords

  • Image Processing, Computer-Assisted
  • Lung Neoplasms
  • Machine Learning
  • Spectrum Analysis, Raman

Identity

PubMed Central ID

  • PMC5661703

Scopus Document Identifier

  • 85032977447

Digital Object Identifier (DOI)

  • 10.1364/BOE.4.002855

PubMed ID

  • 29086544

Additional Document Info

volume

  • 22

issue

  • 10