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

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.