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Updated: Nov 9, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Medical image analysis based on deep learning approach.

Muralikrishna Puttagunta1, S Ravi1

  • 1Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry, India.

Multimedia Tools and Applications
|April 12, 2021
PubMed
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Artificial intelligence, specifically deep learning approaches (DLA), is revolutionizing medical image analysis for disease detection. This review explores DLA

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Medical imaging is crucial for disease detection, monitoring, diagnosis, and treatment evaluation.
  • Artificial neural networks and deep learning are fundamental to modern medical image analysis.
  • Deep Learning Approaches (DLA) represent a rapidly advancing field within medical image analysis.

Purpose of the Study:

  • To present the development and comprehensive analysis of artificial neural networks and DLA in medical imaging.
  • To systematically review DLA applications for classification, detection, and segmentation of medical images.
  • To guide researchers in adapting DLA for enhanced medical image analysis.

Main Methods:

  • Systematic review of existing literature on Deep Learning Approaches in medical image analysis.
Keywords:
ClassificationConvolutional neural networksDeep learningDetectionMedical imagesSegmentation

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  • Analysis of DLA implementations across various medical imaging modalities.
  • Focus on DLA for classification, detection, and segmentation tasks.

    • Main Results:

    • DLA is widely applied in medical imaging for disease detection.
    • Common DLA applications focus on X-ray, CT, mammography, and digital histopathology images.
    • The review provides a structured overview of DLA's capabilities in medical image analysis.

    Conclusions:

    • Deep Learning Approaches offer promising applications in medical imaging.
    • DLA is effective for disease detection, classification, and segmentation across multiple imaging types.
    • This review serves as a guide for future research and development in AI-driven medical image analysis.