Machine Learning for Laser Lesions

Abstract

Oceanit, using its RetinaView software and data collected by the U.S. Air Force developed and trained convolutional neural networks to detect and classify laser lesion injuries in three imaging modalities: Fundus imagery, hyperspectral imagery, and OCT imagery. Networks using the commonly collected Fundus and OCT imagery can detect lesions with 97% accuracy, providing a viable tool for non-expert clinicians with no experience in laser lesion injuries to detect them without additional imaging equipment. Further experiments show it is possible to predict the age of a given lesion, allow for the potential to tie an injury to a specific engagement or event.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 13, 2019
Accession Number
AD1081832

Entities

People

  • Adam R. Boretsky
  • Edward Pier
  • Joel N. Bixler
  • Zachary D. Stoecker-sylvia

Organizations

  • Leidos

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Blood Vessels
  • Computer Vision
  • Convolutional Neural Networks
  • Data Processing
  • Department Of Defense
  • Detection
  • Governments
  • Hyperspectral Imagery
  • Machine Learning
  • Motor Skills
  • Neural Networks
  • Radiation
  • Three Dimensional
  • Two Dimensional
  • United States Government

Readers

  • Computer Vision.
  • Oncology and Biomarker-Based Cancer Detection.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Directed Energy