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BraNet: a mobil application for breast image classification based on deep learning algorithms.

Yuliana Jiménez-Gaona1,2,3, María José Rodríguez Álvarez4, Darwin Castillo-Malla5,4,6

  • 1Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador. ydjimenez@utpl.edu.ec.

Medical & Biological Engineering & Computing
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Summary
This summary is machine-generated.

The BraNet mobile app shows high accuracy in classifying breast ultrasound images, outperforming mammography classification. This highlights the importance of data variety in deep learning for breast cancer detection.

Keywords:
Breast cancerDeep learningMammographyMobil appUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Mobile Health

Background:

  • Mobile health (mHealth) apps leverage AI for breast cancer detection, aiding radiologists and reducing misdiagnoses.
  • Deep learning models are increasingly used for image analysis in oncology.

Purpose of the Study:

  • To develop "BraNet," an open-source mobile application for segmenting and classifying 2D breast images using deep learning.
  • To evaluate the performance of the BraNet app in classifying mammography (DM) and ultrasound (US) breast images.

Main Methods:

  • Developed BraNet using React Native, incorporating pre-trained segmentation (SAM) and classification (ResNet18) models.
  • Trained models using synthetic images generated by an SNGAN model.
  • Implemented a client-server architecture for iOS and Android devices.
  • Conducted a reader study with two radiologists on 290 RoI images, assessing agreement using the kappa coefficient.

Main Results:

  • BraNet achieved high accuracy in classifying benign and malignant US images (94.7%/93.6%) compared to DM (Training I: 80.9%/76.9%; Training II: 73.7%/72.3%).
  • Radiologists' accuracy was 29% for DM and 70% for US, with higher performance in US classification.
  • Kappa values indicated fair agreement (0.3) for DM and moderate agreement (0.4) for US images between readers.

Conclusions:

  • The BraNet app demonstrates superior performance in classifying breast ultrasound images.
  • Data quantity and variety, particularly for mammography with diverse BI-RADS categories, are crucial for accurate deep learning model performance in breast cancer detection.