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A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging.

Maryam Momtahen1, Farid Golnaraghi1

  • 1School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Near-infrared Scan (NIRscan) technology combined with a multitask convolutional neural network (CNN) for improved breast cancer detection. The advanced model enhances tumor localization and classification accuracy, offering a promising tool for early breast cancer screening.

Keywords:
NIRscanbreast cancer imagingclassificationconvolutional neural networksdata augmentationdeep learningimage reconstructiontumor localization

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Area of Science:

  • Biomedical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Mammography faces challenges in detecting breast cancer in dense tissues.
  • Early and accurate tumor localization is crucial for effective breast cancer treatment.

Purpose of the Study:

  • To enhance tumor localization accuracy and efficiency using Near-infrared Scan (NIRscan) and a convolutional neural network (CNN).
  • To develop a multitask CNN for simultaneous image reconstruction and classification of tumor characteristics.

Main Methods:

  • Utilized a NIRscan probe and a CNN model for image processing of breast phantoms.
  • Applied data augmentation techniques to increase sample size.
  • Developed and implemented a multitask CNN for image reconstruction and classification tasks.

Main Results:

  • Achieved significant improvements in image reconstruction with low RMSE (0.0624) and MAE (0.0360).
  • Demonstrated high R² (0.9704) and Fuzzy Jaccard Index (0.9121) for image reconstruction.
  • Attained excellent classification accuracies: 100% for depth, 92.86% for length, and perfect F1 Score for health status.

Conclusions:

  • NIRscan technology coupled with a multitask CNN shows significant promise for improving breast cancer detection.
  • The combined approach enhances diagnostic capabilities, supporting real-time screening and diagnostic workflows.
  • This technology offers a potential advancement in overcoming limitations of traditional breast cancer imaging.