Fusing clinical and image data for detecting the severity of breast cancer by a novel hierarchical approach

ABSTRACT

In this paper, we developed an innovative approach combining clustering and classification routines to detect breast cancer severity (stage) and recognise whether or not it metastasises. We use Fuzzy C-mean to cluster data and a proper classification routine to recognise the severity of cancer for each cluster. In other words, we use the divide-and-conquer rule to overcome the nonlinearity of relations between features. Moreover, to have a more accurate classification in the test or real data, we impose the fuzzy membership of each data to a cluster along with other features as the set of input into the classification method. Another advantage of our research study is to use both clinical and image features and to extract new features using principal component analysis (PCA) for the classification phase. Whereas a patient might belong to more than one cluster, the results of all corresponding classification methods for the respective patient are appropriately combined to end up with the stage of the cancerous patient. Ultimately, to investigate the efficiency of the proposed hybrid approach, we use seven real data sets with both clinical and image data.

KEYWORDS:

• Breast cancer
• feature selection
• image processing
• classification
• clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).