Applying a new feature fusion method to classify breast lesions

Developing a computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role in improving MRI screening efficacy. This study demonstrates that extracting features from both spatial and frequency domains, and applying an efficient combination of data reduction and classifier methods, had the potential to significantly improve accuracy in classifying between malignant and benign breast masses. By applying our CAD scheme to the testing dataset, we obtained an accuracy of 83.1% for the best combination of data reduction and classification (DNE-SVM).