Machine Learning-Aided Automatic Detection of Breast Cancer

Abstract

The expeditious progress of machine learning, especially the deep learning techniques, keep propelling the medical imaging community's heed in applying these techniques in improving the accuracy of cancer screening. Among various types of cancers, breast cancer is the most detrimental disease affecting women today. The prognosis of such types of disease becomes a very challenging task for radiologists due the huge number of cases together with careful and thorough examination it demands. The constraints of present CAD open up a need for new and accurate detection procedures. Deep learning approaches have gained a tremendous recognition in the areas of object detection, segmentation, image recognition, and computer vision. Precise and premature detection and classification of lesions is very critical for increasing the survival rates of patients. Recent CNN models are designed to enhance radiologists' understandings to identify even the least possible lesions at the very early stage.

A novel SVM Kernel Classifier Technique using Support Vector Machine for Breast Cancer Classification

“…The accuracy ratings for all available approaches are more than 90% [4] even after a significant reduction in the number of characteristics employed. Random Forest, K-Nearest Neighbors, Nave Bayes and Decision Tree [5] are a few of the categorization algorithms now in use. The area under the corrected operational characteristic curve, ambiguity matrix, recall score and accuracy were the most accurate accuracy measures.…”