Deep learning algorithms for the early detection of breast cancer: A comparative study with traditional machine learning

Deep learning has been widely applied in breast cancer screening to analyze images obtained from X-rays, ultrasound, magnetic resonances, and biopsies. This study suggests that deep learning can also be used to prescreen for cancer by analyzing heterogeneous data obtained from demographic and anthropometric information of patients, biological markers from routine blood samples, and relative risks from meta-analysis and international databases. In this document, feature selection is applied to a database of 64 women diagnosed with breast cancer and a counterfactual group of 52 healthy women, to identify the best predictors of cancer prescreening. The best predictors are used in k-fold Monte Carlo cross-validation experiments that compare deep learning against machine learning. The results indicate that a deep learning architecture that is fine-tuned using feature selection has the lowest false negative rate (i.e., the lowest Type II errors) and can effectively distinguish between patients with and without cancer. Consequently, deep learning—compared to traditional machine learning—promotes a more accurate detection of malignancies, hence reducing the risk of increased tumor size and cancer spreading to nearby or distant lymph nodes, tissues, or organs, due to late detection. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions, as indicated by the lowest standard deviation of its performance metrics. These findings indicate that deep learning algorithms applied to cancer prescreening offer a radiation-free, non-invasive, and an affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer prescreening helps to identify individuals who may require imaging-based screening, can encourage self-examination, and decreases the psychological drawbacks associated with false positives in cancer screening, ultimately leading to earlier detection of malignancy and reducing the healthcare and societal burden associated with cancer treatment.