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Related Experiment Video

Updated: Sep 13, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

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.

Diagnostics (Basel, Switzerland)

|July 29, 2025

Summary

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

Keywords:

breast cancer deep learning image processing tumor

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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.