Novel architecture with selected feature vector for effective classification of mitotic and non-mitotic cells in breast cancer histology images

Abstract

The paper focuses on the detection of mitosis in breast cancer. Detection methods in vogue rely heavily on visual inspection and assessment of histology images by trained pathologists, so the validation of results is a time-consuming process. Even accuracy wise, there remains room for improvement. This calls for high accuracy automated systems to replace manual assessments. Mitotic count is important because it provides us information about the aggressiveness of the tumour cells i.e. how rapidly it is spreading in the human body. To address this research gap, the current study is devoted to construct a novel framework utilizing neural network-based concepts along with reduced feature vectors and multiple machine learning techniques to classify the mitotic and non-mitotic cells. The dominant features in the detection of mitosis are texture features, therefore the texture of cells is used to derive the efficient reduced feature vectors which plays a significant role in identifying cancerous cells. During the feature engineering process, the features that distinguish a cancer cell from the normal cells were identified. Using these features as the base features, extraction with multiple techniques like GLCM, LBP and LTP along with classification algorithms like SVM, Naive Bayes and Random Forest were applied to the source dataset of the images, whose ground truth reference was available. Inspiring from Neural Networks, the proposed architecture allocates different weights to different features which increases the productivity of the model. The proposed framework is a new and intelligent version of ensemble learning which makes ensemble learning a more powerful tool for accurate and high-throughput results. The proposed method outperforms techniques already reported in the literature for the detection of mitotic cells in MITOS-12, AMIDA 13, MITOS-14 and TUPAC16.