High-performance breast cancer diagnosis method using hybrid feature selection method

One of the primary causes of the women death is breast cancer. Accurate and early breast cancer diagnosis plays an essential role in its treatment. Computer Aided Diagnosis (CAD) system can be used to help doctors in the diagnosis process. This study presents an efficient method to performance improvement of the breast cancer diagnosis CAD system using thermal images.

The research strategy in the proposed CAD system is using efficient algorithms in feature extraction and classification phases, and new efficient feature selection algorithm. In the feature extraction phase, the Segmentation Fractal Texture Analysis (SFTA) algorithm that is a texture analysis algorithm is used.This algorithm utilizes two-threshold binary decomposition. In the feature selection phase, the developed feature selection algorithm, which is hybrid of binary grey wolf optimization algorithm and firefly optimization algorithm, is applied to extracted features. Then, the kNN, SVM, and DTree classification techniques are applied to check whether the selected features are efficiently discriminated the group successfully with minimal misclassifications.

The DMR database is utilized for performance evaluation of the proposed method. The results indicate that the obtained accuracy, specificity, sensitivity, and MCC are 97, 96, 98, and 94.17 %, respectively.

The developed breast cancer diagnosis method has advantages compared to other breast cancer diagnosis using thermal images.