Classification and quantitative analysis of histopathological images of breast cancer

This paper provides a robust and reliable computational technique in cancer research. The morphological features analysis is always considered as an important tool to analyse the abnormality in cellular organisation of cells. These features of malignant cells show changes in patterns as compared to that of benign cells. However, manual analysis is time-consuming and varies with perception level of the expert pathologist. To assist the pathologists in analysing, morphological features are extracted, and two datasets are prepared from the group cells and single cells images for benign and malignant categories. Finally, classification is performed using supervised classifiers. In the present investigation, three classifiers [artificial neural network (ANN), k-nearest neighbour (k-NN) and support vector machine (SVM)] are trained using publicly available breast cancer datasets. The result of performance indicators for benign and malignant images was calculated and it is found that the classification accuracy achieved by the single cells dataset is better than the group cells. Furthermore, it is established that ANN provides a better result for both datasets than the other two (k-NN and SVM). The proposed method of the computer-aided diagnosis system for the classification of benign and malignant cells provides better accuracy than the other existing methods.