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Related Experiment Video

Updated: Nov 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Internet of medical things embedding deep learning with data augmentation for mammogram density classification.

Tariq Sadad1, Amjad Rehman Khan2, Ayyaz Hussain3

  • 1Department of Computer Science & Software Engineering, International Islamic University, Islamabad, Pakistan.

Microscopy Research and Technique
|April 28, 2021
PubMed
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This study developed an automated breast density (BD) detection system using deep learning on mammograms. The Internet of Medical Things (IoMT) framework achieved 90.47% accuracy, aiding early breast cancer (BC) detection.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer (BC) is a prevalent disease affecting millions of women globally.
  • Accurate breast density (BD) assessment is crucial for early BC detection.
  • Computer-aided diagnosis (CAD) systems can assist radiologists in identifying BD.

Purpose of the Study:

  • To develop and evaluate an automated system for breast density detection using mammograms.
  • To leverage Internet of Medical Things (IoMT) supported devices for real-time analysis.
  • To improve the speed and precision of BD assessment for BC screening.

Main Methods:

  • Utilized transfer learning with two pretrained deep convolutional neural network models: DenseNet201 and ResNet50.
  • Applied preprocessing techniques to refine mammogram images, enhancing relevant regions.
Keywords:
Internet of Medical Things (IoMT)breast densitycancercomputer-aided diagnosishealthcaremammographymasses

Related Experiment Videos

Last Updated: Nov 7, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
  • Trained and tested models on 322 mammograms from the Mammogram Image Analysis Society dataset, categorized into fatty, dense, and glandular types.

    • Main Results:

    • The DenseNet201 model achieved an overall classification accuracy of 90.47% for breast density detection.
    • The ResNet50 model also demonstrated strong performance in classifying breast density.
    • The developed framework successfully automated the identification of different breast densities from mammograms.

    Conclusions:

    • The proposed IoMT-based CAD framework effectively automates breast density detection with high accuracy.
    • This automated approach can significantly assist radiologists, leading to faster diagnoses and improved patient outcomes.
    • The system offers a valuable tool for rapid BD identification, supporting timely breast cancer screening and management.