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Microscopic medical image classification framework via deep learning and shearlet transform.

Hadi Rezaeilouyeh1, Ali Mollahosseini1, Mohammad H Mahoor1

  • 1University of Denver , Department of Electrical and Computer Engineering, 2155 East Wesley Avenue, Denver, Colorado 80208, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|November 23, 2016
PubMed
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This study introduces a new framework for cancer detection using deep learning and shearlet transforms. Combining original images with shearlet features improves diagnostic accuracy and generalizability for medical image analysis.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Machine Learning

Background:

  • Cancer is a leading cause of death, necessitating advanced diagnostic tools.
  • Current computer-aided diagnosis methods rely on hand-crafted features, which often lack generalizability.
  • Convolutional Neural Networks (CNNs) offer an alternative by learning features directly from data.

Purpose of the Study:

  • To develop a novel framework for enhanced breast cancer detection and prostate Gleason grading.
  • To integrate shearlet transform features with CNNs for improved medical image analysis.
  • To overcome the limitations of hand-crafted features in cancer diagnosis.

Main Methods:

  • A CNN framework was developed, incorporating original medical images.
Keywords:
breast cancerdeep neural networkmicroscopic imagesprostate cancershearlet transform

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  • Shearlet transform was applied to extract magnitude and phase information from images.
  • Extracted shearlet features were fed alongside original images into the CNN for training.

    • Main Results:

    • The proposed framework demonstrated improved accuracy in cancer detection and Gleason grading.
    • Integrating shearlet features enhanced the generalizability of the CNN model.
    • The approach outperformed existing methods that utilize only hand-crafted features.

    Conclusions:

    • The combination of CNNs and shearlet transform features offers a promising advancement in medical image analysis.
    • This method effectively addresses the challenge of limited medical data in deep learning applications.
    • The framework shows potential for more accurate and generalizable early-stage cancer diagnosis.