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Related Experiment Video

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Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep

Adnan Rafiq1, Arfan Jaffar1, Ghazanfar Latif2,3

  • 1Department of Computer Science & IT, Superior University, Lahore 54000, Pakistan.

Diagnostics (Basel, Switzerland)
|March 13, 2025
PubMed
Summary

A deep learning model using DenseNet121 achieved high accuracy in classifying breast cancer from histology images. This technology shows promise for improving breast cancer diagnosis and treatment planning.

Keywords:
breast cancer classificationdeep featuresdeep learningdeep neural networkdensenet121different magnification levelshistopathology images

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Oncology

Background:

  • Breast cancer is a leading cause of cancer mortality globally.
  • Accurate histological classification is crucial for effective breast cancer diagnosis and treatment.

Purpose of the Study:

  • To develop and evaluate a deep learning model for breast cancer detection and classification.
  • To assess the model's performance on whole-slide histopathology images.

Main Methods:

  • A DenseNet121-based deep learning model was proposed.
  • Experiments were conducted using the BreakHis dataset of histopathology images.

Main Results:

  • The model achieved 98.50% accuracy and 0.98 AUC for binary classification.
  • For multi-class classification, the model obtained 92.50% accuracy and 0.94 AUC.

Conclusions:

  • The deep learning model demonstrates superior performance in distinguishing benign from malignant tumors.
  • The study underscores the potential of AI in advancing breast cancer diagnostics.