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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.

Asadulla Ashurov1, Samia Allaoua Chelloug2, Alexey Tselykh3

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Life (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for breast cancer histopathological image classification. Modified convolutional neural network (CNN) models with attention mechanisms achieved high accuracy, improving breast cancer diagnosis.

Keywords:
CNNattention mechanismbenignbreast cancerclassificationhistopathology imagemagnification levelmalignanttransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Breast cancer is a major global health concern, necessitating advanced diagnostic tools.
  • Deep learning has shown promise in revolutionizing medical image analysis for disease detection.
  • Accurate histopathological image classification is crucial for effective breast cancer diagnosis and treatment planning.

Purpose of the Study:

  • To develop and evaluate a novel deep learning approach for breast cancer histopathological image classification.
  • To enhance the interpretability and robustness of diagnostic models using attention mechanisms.
  • To improve the accuracy of breast cancer detection in complex cases.

Main Methods:

  • Utilized transfer learning with pre-trained deep convolutional neural network (CNN) models: Xception, VGG16, ResNet50, MobileNet, and DenseNet121.
  • Integrated the convolutional block attention module (CBAM) to augment CNN models, focusing on localized features.
  • Fine-tuned the models and evaluated their performance using accuracy, precision, recall, and F1 score on the BreakHis dataset.

Main Results:

  • Attention mechanisms (AM) combined with the Xception model achieved test accuracies of 99.2% and 99.5%.
  • The DenseNet121 model with AMs demonstrated a high test accuracy of 99.6%.
  • The proposed methods outperformed previously studied approaches in breast cancer diagnosis.

Conclusions:

  • The novel deep learning approach, incorporating attention mechanisms, significantly enhances breast cancer histopathological image classification accuracy.
  • This method offers a robust and interpretable tool for computer-assisted pathological diagnosis.
  • The findings suggest a promising advancement in automated breast cancer detection systems.