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Updated: Nov 18, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning based search engine for biomedical images using convolutional neural networks.

Richa Mishra1, Surya Prakash Tripathi2,3

  • 1Computer Science & Engineering Department, Institute of Engineering & Technology, Lucknow, 226021 India.

Multimedia Tools and Applications
|February 8, 2021
PubMed
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This study introduces a novel deep learning model to improve biomedical image search. The fused vector-space and convolutional neural network approach enhances query matching, especially for mismatched queries.

Area of Science:

  • Biomedical Informatics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Efficient retrieval of biomedical images is challenging, particularly with query-mismatch issues.
  • Existing vector-space models handle query mismatch but lack relational keyword analysis and search space evaluation.
  • Deep learning offers potential for advanced image search but requires integration with traditional models.

Purpose of the Study:

  • To develop an enhanced deep learning-based fusion model for biomedical image search.
  • To address the limitations of existing vector-space models in handling relational keyword details.
  • To improve the accuracy and efficiency of biomedical image query similarity matching.

Main Methods:

  • Proposed a novel fusion model combining a vector-space model with convolutional neural networks (CNNs).
Keywords:
Biomedical imagesDeep learningSearch engineWebsites

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  • Transformed the vector-space model into a classification model to enable deep learning integration.
  • Trained the deep learning model to function as an advanced search engine for biomedical images.

    • Main Results:

    • The proposed fusion model significantly improves biomedical image query similarity matching.
    • Demonstrated superior performance compared to existing models in extensive experimental evaluations.
    • Effectively handles query-mismatch scenarios by integrating relational keyword information.

    Conclusions:

    • The deep learning-based fusion model offers a significant advancement in biomedical image search engine technology.
    • Integrating vector-space models with deep learning, specifically CNNs, enhances retrieval accuracy.
    • The developed model provides a robust solution for the ill-posed problem of efficient biomedical image retrieval.