Artificial intelligence-based classification of breast cancer using cellular images

Abstract

Detection and classification of breast cancer at the cellular level is one of the most challenging problems. Since the morphology and other cellular features of cancer cells are different from normal heathy cells, it is possible to classify cancer cells and normal cells using such features. Although various artificial intelligence (AI) techniques including least square support vector machine (LS-SVM) have been used for pattern recognition, their use in classifying breast cancer from cellular images has yet not been established. In this communication, we developed an alternative approach using various AI techniques to classify breast cancer and normal cells using cellular image texture features extracted from cell images of various breast cancer cell lines like MCF-7, MDAMB-231 and the human normal breast cell line MCF-10A. Applying pattern recognition techniques upon various human breast cancer/normal cell images, we successfully performed cellular image segmentation, texture based image feature extraction and subsequent classification of cancer and normal breast cells. Four different AI techniques: K^th nearest neighbour (KNN), artificial neural network (ANN), support vector machine (SVM) and LS-SVM were applied to classify cancer using optimal features obtained from cell segmented images. Our results demonstrated that LS-SVM with both radial basis function (RBF) and linear kernel classifier had the highest classification rate of 95.34% among all. Thus, our LS-SVM classifier was found to be a suitable trained model that could classify the cancer and normal cells using cell image features in a short time unlike other approaches reported so far.