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Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole

Aadhi Aadhavan Balasubramanian1, Salah Mohammed Awad Al-Heejawi2, Akarsh Singh2

  • 1Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.

Cancers
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

This study uses ensemble deep learning to analyze breast cancer histopathology images. The AI model achieved high accuracy in classifying cancer types, aiding diagnosis and potentially improving patient outcomes.

Keywords:
artificial intelligencebreast cancer diagnosiscomputer visiondigital pathologyensemble deep learningfoundation modelshistopathology imagesimage processing

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Area of Science:

  • Computational pathology
  • Medical imaging analysis
  • Artificial intelligence in oncology

Background:

  • Accurate breast cancer diagnosis and classification are crucial for effective patient management.
  • Histopathology image analysis is a cornerstone of breast cancer diagnosis.
  • Deep learning offers potential for automating and enhancing image analysis.

Purpose of the Study:

  • To develop and evaluate an ensemble deep learning approach for breast cancer histopathology image classification.
  • To assess the performance of the proposed method on two diverse datasets (BACH and BreakHis).
  • To investigate the utility of a novel image patching technique for high-resolution image analysis.

Main Methods:

  • Ensemble deep learning models (VGG16, ResNet50, ResNet34) were utilized.
  • A novel image patching technique was introduced for preprocessing high-resolution WSIs.
  • The models were trained and tested on the BACH and BreakHis datasets using five-fold cross-validation.

Main Results:

  • The ensemble model achieved a patch classification accuracy of 95.31% on the BACH dataset.
  • The ensemble model achieved a WSI image classification accuracy of 98.43% on the BreakHis dataset.
  • The method demonstrated high precision in classifying various breast cancer subtypes.

Conclusions:

  • Ensemble deep learning, coupled with image patching, is a powerful tool for breast cancer histopathology image analysis.
  • The developed approach shows significant promise for advancing AI-driven breast cancer diagnosis.
  • This research can contribute to improved patient outcomes and reduced healthcare burdens.