Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

The application of artificial intelligence techniques in computer aided detection and diagnosis problems has been among the most promising areas with interest from the scientific community and healthcare industry. Recently, deep learning has become the prime tool for such application with many studies focusing on developing variants that optimize diagnostic performance. Despite the widely accepted success of this class of techniques in this application by the scientific community, it is not prudent to consider it as the only tool available for such purpose. In particular, statistical machine learning offers a variety of techniques that can also be applied at a much lower computational cost. Unfortunately, the results from both strategies cannot be directly compared due to the differences in experimental setups and datasets used in available research studies. Therefore, we focus in this study on this direct comparison using the same dataset and similar data preprocessing as the input to both. We compare statistical machine learning to deep learning in the context of computer-aided detection of breast cancer from mammographic images. The results are compared using diagnostic performance metrics and suggest that simpler statistical machine learning techniques may provide better performance with simpler architectures that allow explanation of results.

@article{Alhussan2021,
  title     = {Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {10},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Amel A. Alhussan and Nagwan M. Abdel Samee and Vidan F. Ghoneim and Yasser M. Kadah},
  doi       = {10.14569/IJACSA.2021.0121033},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121033}
}