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Updated: Jul 20, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep Learning for Optical Sensor Applications: A Review.

Nagi H Al-Ashwal1,2, Khaled A M Al Soufy1,2, Mohga E Hamza1

  • 1Department of Physics, The American University in Cairo, New Cairo 11835, Egypt.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) enhances optical sensor accuracy and reduces noise. Integrating DL addresses challenges like large datasets and high costs, paving the way for advanced intelligent sensing applications.

Keywords:
autoencodersconvolutional neural networkdeep learningdeep neural networkoptical sensors

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Area of Science:

  • Optical sensing technologies
  • Artificial intelligence
  • Data processing

Background:

  • Optical sensors are crucial for intelligent sensing in diverse fields like process monitoring, quality control, and security.
  • Traditional optical sensors face challenges including large data volumes, slow processing, and high costs.
  • Deep learning (DL) offers potential solutions to these limitations.

Purpose of the Study:

  • To review recent studies integrating deep learning algorithms with optical sensor applications.
  • To highlight promising directions for DL in optical sensing.
  • To propose future research avenues for DL-enhanced optical sensors.

Main Methods:

  • Literature review of recent studies on DL in optical sensing.
  • Analysis of DL algorithm integration with optical sensor data.
  • Identification of current challenges and future opportunities.

Main Results:

  • Deep learning significantly improves accuracy and reduces noise in optical sensor data.
  • Integration of DL mitigates challenges associated with large datasets and processing speeds.
  • DL applications are expanding across various sectors including pollution monitoring and defense.

Conclusions:

  • Deep learning is a key technology for advancing optical sensor capabilities.
  • Further research into DL algorithms can unlock new potentials for intelligent sensing.
  • Addressing data and cost challenges through DL integration is vital for future optical sensor development.