Breast cancer detection employing stacked ensemble model with convolutional features

This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.PMID:38160347 | DOI:10.3233/CBM-230294

Source: Cancer Biomarkers - December 31, 2023 Category: Cancer & Oncology Authors: Hanen Karamti Raed Alharthi Muhammad Umer Hadil Shaiba Abid Ishaq Nihal Abuzinadah Shtwai Alsubai Imran Ashraf Source Type: research