Classification of breast cancer abnormalities using mixed machine learning and deep learning methods

L. Guillermo Rodriguez-Lopez; J. Angel Gonzalez-Fraga; Vitaly Kober

doi:10.1117/12.3028365

30 September 2024 Classification of breast cancer abnormalities using mixed machine learning and deep learning methods

L. Guillermo Rodriguez-Lopez, J. Angel Gonzalez-Fraga, Vitaly Kober

Author Affiliations +

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131371H (2024) https://doi.org/10.1117/12.3028365
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

Mammography is a standardized imaging technique crucial for the early detection of breast cancer, primarily aimed at identifying abnormalities, or ’findings’ in the breasts that cannot be detected through palpation. This study proposes different models for classifying breast abnormalities, integrating machine learning and deep learning methods to improve classification rates. The proposed methodology involves several key steps: preprocessing of image datasets, training of base classification models, and construction of a meta-classifier. By enhancing the performance of individual classifiers, the model is benchmarked against various machine learning models. The evaluation of this method is conducted using the CBIS-DDSM mammography dataset, demonstrating its effectiveness in improving classification accuracy and reliability. The hybrid approach leverages convolutional neural networks (CNNs) such as VGG16, VGG19, and DenseNet121 for feature extraction, followed by machine learning algorithms for final classification. The VGG16 network, combined with machine learning techniques, aimed to surpass the results obtained by VGG19. Ensemble methods, particularly Voting and Stacking Classifiers, showed that combining VGG16 and DenseNet121 yielded the highest accuracy of 91.66%. These findings underscore the potential of hybrid models in breast cancer classification, offering significant improvements over single classifiers and providing valuable insights for future research in medical image analysis.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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L. Guillermo Rodriguez-Lopez, J. Angel Gonzalez-Fraga, and Vitaly Kober "Classification of breast cancer abnormalities using mixed machine learning and deep learning methods", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131371H (30 September 2024); https://doi.org/10.1117/12.3028365

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