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BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network.

Roslidar Roslidar1,2,3, Mohd Syaryadhi2, Khairun Saddami2

  • 1Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia.

Mathematical Biosciences and Engineering : MBE
|February 9, 2022
PubMed
Summary

A novel mobile deep learning model, BreaCNet, achieves 100% accuracy for early breast cancer detection using thermal imaging. This mobile neural network (CNN) enhances diagnostic sensitivity and specificity, prioritizing user data privacy through on-device processing.

Keywords:
breast cancermobile cnnsegmentationself-screeningthermogram

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early breast cancer detection is crucial for improved patient outcomes.
  • Mobile-based diagnostic tools require accurate and efficient deep learning models.
  • Thermography offers a non-invasive imaging modality for breast cancer screening.

Purpose of the Study:

  • To develop a mobile neural network (BreaCNet) for early breast cancer detection using breast thermograms.
  • To create an effective segmentation algorithm for identifying the region of interest (ROI) in thermograms.
  • To implement a mobile convolutional neural network (CNN) classifier with high accuracy and efficiency.

Main Methods:

  • Developed BreaCNet, integrating a segmentation algorithm (edge detection, polynomial fitting) with a modified ShuffleNet classifier.
  • Modified ShuffleNet by adding a convolutional layer with 1028 filters, resulting in 6.1 million parameters and a 22 MB model size.
  • Evaluated the model's performance using simulation results, focusing on accuracy, sensitivity, and specificity.

Main Results:

  • The modified ShuffleNet classifier alone achieved 72% accuracy.
  • Integrating the segmentation algorithm with the classifier boosted accuracy to 100%.
  • BreaCNet significantly improved diagnostic accuracy, increasing sensitivity from 43% to 100% and specificity to 100%.

Conclusions:

  • BreaCNet demonstrates high diagnostic accuracy for early breast cancer detection using mobile thermography.
  • Utilizing the ROI through segmentation enhances classifier performance.
  • On-device inference is recommended for BreaCNet to ensure data privacy and reliable performance.