Hybrid Convolution Neural Network in Classification of Cancer in Histopathology Images.

Abstract

Cancer statistics in 2020 reveals that breast cancer is the most common form of cancer among women in India. One in 28 women is likely to develop breast cancer during their lifetime. The mortality rate is 1.6 to 1.7 times higher than maternal mortality rates. According to the US statistics, about 42,170 women in the US are expected to die in 2020 from breast cancer. The chance of survival can be increased through early and accurate diagnosis of cancer. The pathologists manually analyze the histopathology images using high-resolution microscopes to detect the mitotic cells. This is a time-consuming process because there is a minute difference between the normal and mitotic cells. To overcome these challenges, an automatic analysis and detection of breast cancer by using histopathology images play a vital role in prognosis. Earlier researchers used conventional image processing techniques for the detection of mitotic cells. These methods were found to be producing results with low accuracy and time-consuming. Therefore, several deep learning techniques were adopted by researchers to increase the accuracy and minimize the time. The hybrid deep learning model is proposed for selecting abstract features from the histopathology images. In the proposed approach, we have concatenated two different CNN architectures into a single model for effective classification of mitotic cells. Convolution neural network (CNN) automatically detects efficient features without human intervention and classifies cancerous and non-cancerous images using a hybrid fully connected network. It is a computationally efficient, very powerful, and efficient model for performing automatic feature extraction. It detects different phenotypic signatures of nuclei. In order to enhance the accuracy and computational efficiency, the histopathology images are preprocessed, segmented, and feature extracted through CNN and fed into a hybrid CNN for classification. The hybrid CNN is obtained by concatenating two CNN models; together, this is called model leveraging. Model averaging can be improved by weighting the contributions of each sub-model to the combined prediction by the expected performance of the sub-model. The proposed hybrid CNN architecture with data preprocessing with median filter and Otsu-based segmentation technique is trained using 50,000 images and tested using 50,000 images. It provides an overall accuracy of 98.9%.