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Updated: Sep 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep Learning Approaches for Automatic Localization in Medical Images.

H Alaskar1, A Hussain2,3, B Almaslukh1

  • 1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia.

Computational Intelligence and Neuroscience
|July 11, 2022
PubMed
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Deep learning (DL) significantly improves medical image analysis for earlier disease detection. This review focuses on deep neural networks (DNNs) for object localization in medical imaging, highlighting research gaps and future directions.

Area of Science:

  • Computer Vision
  • Medical Imaging Analysis
  • Artificial Intelligence

Background:

  • Deep learning (DL) has revolutionized computer vision since 2012, surpassing traditional machine learning models.
  • DL enables highly accurate image interpretation, showing promise for early disease diagnosis and treatment.
  • Object localization using deep neural networks (DNNs) is a rapidly advancing area in medical image analysis.

Purpose of the Study:

  • To review the implementation of DNNs for object localization in medical images.
  • To summarize recent studies and identify research gaps in DNN-based medical image localization.
  • To provide insights for future research directions in this field.

Main Methods:

  • Review of recent literature on DNN applications for medical image object localization.

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  • Validation of DNN efficacy on established benchmarks.
  • Analysis of dominant DNN architectures used in current research.

    • Main Results:

    • DNNs demonstrate superior performance in object localization tasks within medical imaging compared to conventional methods.
    • The review synthesizes current research, identifying key trends and successful applications.
    • Efficacy of DNNs is validated through benchmark performance.

    Conclusions:

    • DNNs are highly effective for object localization in medical images, facilitating earlier disease detection.
    • Further research is needed to address challenges and explore future developments in DNN-based medical image analysis.
    • This review provides a foundation for future studies aiming to advance medical image localization techniques.