A general type-II similarity based model for breast cancer grading with FTIR spectral data

Breast cancer is one of the most frequently occurring cancers among women throughout the world. In breast cancer prognosis, grading plays an important role. In this paper, we apply a novel method based on type-II fuzzy logic to Fourier Transform Infra-red Spectroscopy based breast cancer spectral data for the classification of breast cancer grade. A FTIR spectral data set consisting of 14 cases of breast cancer has been used. A zSlices based type-II fuzzy logic approach has been used to create prototype models for the classification of unseen breast cancer cases. The prototype models are used with a similarity measure to classify unseen cases of cancer. We have shown that the T-II similarity based model is a promising methodology for classification.