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Breast Cancer Classification with Various Optimized Deep Learning Methods.

Mustafa Güler1, Gamze Sart2, Ömer Algorabi3

  • 1Engineering Sciences Department, Engineering Faculty, Istanbul University-Cerrahpasa, Istanbul 34320, Türkiye.

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Deep learning models show promise for breast cancer classification. DenseNet201 achieved 89.4% accuracy, demonstrating AI

Keywords:
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Area of Science:

  • Medical data analysis
  • Image processing
  • Artificial intelligence in medicine

Background:

  • Increasing incidence of breast cancer necessitates advanced diagnostic tools.
  • Distinguishing benign from malignant tumors is crucial for patient outcomes.
  • Deep learning applications in medicine have shown significant success.

Purpose of the Study:

  • To evaluate the efficacy of 11 deep learning algorithms for breast cancer classification.
  • To compare the performance of various deep learning models on pathological breast biopsy images.
  • To identify the most accurate deep learning model for classifying benign and malignant breast tumors.

Main Methods:

  • Utilized 11 deep learning algorithms including ResNet, VGG16, DenseNet201, and others.
  • Classified 10,000 breast biopsy images (6172 benign, 3828 malignant).
  • Employed an 80% training, 10% validation, and 10% testing data split.

Main Results:

  • DenseNet201 achieved the highest classification accuracy at 89.4%.
  • DenseNet201 demonstrated strong performance with 88.2% precision, 84.1% recall, and 86.1% F1 score.
  • An AUC score of 95.8% was recorded for DenseNet201.

Conclusions:

  • Deep learning algorithms hold significant potential for accurate breast cancer classification.
  • DenseNet201 emerged as a highly effective model for this task.
  • Future research should explore multi-modal data integration and ensemble methods to enhance clinical applicability.