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Related Experiment Video

Updated: Jan 13, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Mammogram Analysis with YOLO Models on an Affordable Embedded System.

Anongnat Intasam1, Nicholas Piyawattanametha2, Yuttachon Promworn1

  • 1Department of Biomedical Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Ladkrabang, Bangkok 10520, Thailand.

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PubMed
Summary
This summary is machine-generated.

The YOLOv11n model on an NVIDIA Jetson Nano offers accurate mammographic lesion detection for resource-limited settings. This affordable system provides comparable performance to expensive GPU setups, enhancing early breast cancer screening accessibility.

Keywords:
CADYOLOv10YOLOv11YOLOv5YOLOv8artificial intelligencebreast cancer diagnosiscomputer-aided detectionmammogramobject detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer-Aided Detection (CAD)

Background:

  • Breast cancer is a leading global cause of female mortality.
  • Mammography is crucial for early detection but faces accessibility challenges in resource-limited areas due to a lack of skilled radiologists and advanced tools.
  • Deep learning-based CAD systems can automate lesion detection and classification, aiding radiologists.

Purpose of the Study:

  • To investigate the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (RT-DETR) for detecting mammographic lesions.
  • To evaluate these models on an affordable embedded system, specifically the NVIDIA Jetson Nano.
  • To assess the feasibility of deploying advanced CAD systems in low-resource clinical environments.

Main Methods:

  • A custom web-based annotation tool was developed to ensure high mammogram labeling accuracy.
  • A dataset comprising 3169 patients from Thailand was utilized, with annotations provided by three expert radiologists.
  • Lesions were categorized into six types: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM).

Main Results:

  • The YOLOv11n model demonstrated optimal performance on the NVIDIA Jetson Nano, achieving an accuracy of 0.86.
  • The YOLOv11n model achieved an inference speed of 6.16 ± 0.31 frames per second on the Jetson Nano.
  • Comparative analysis showed that the Jetson Nano system offers comparable detection performance to a graphics processing unit (GPU)-powered system at a significantly lower cost.

Conclusions:

  • This study is among the first to integrate advanced YOLO versions for embedded mammography deployment.
  • The findings demonstrate the feasibility of using affordable embedded systems for CAD in low-resource settings.
  • This approach has the potential to significantly improve mammographic screening accessibility and clinical applications in underserved areas.