Breast cancer tumor classification using machine learning algorithms
Breast cancer is one of the most deadly diseases, even in this modern era. However, it can be cured if it is detected in the early stages. The main issue is that breast cancer does not have very prominent symptoms in the initial stages. These cancers can be screened by different modalities and then manually screened by radiologists. The manual screening of cancer tests by radiologists is prone to error and time consuming, therefore it is highly advantageous to have a network of machine learning analysis support systems to assist radiologists’ decision. Mammography is a common screening modality used for screening of breast cancer. In this paper, textural features are extracted from digital mammograms collected from the Digital Database for Screening Mammography (DDSM). These features are used for the classification of the tumor using different machine learning algorithms. The performance of the classifiers is evaluated using Accuracy, Precision, Recall, F1- Score, Training time, Receiver Operating Curve (ROC), Area under the Curve (AUC) and different Errors.