Breast cancer classification using deep neural network

Abstract

Over the last few decades, cases of breast cancer have increased enormously. It is the second most popular cause of deaths in women in both developed and undeveloped countries. 8 out of 100 women face this popular and dangerous disease in their life period. The only way to cure this disease is to detect breast cancer at early stages. Delay in identifying breast cancer leads to an increase in the death rate. An appropriate data representation determines the performance of classification systems. In this work, we have done some classification on Breast Cancer histopathological images from publically available Break-His dataset using machine learning and deep learning techniques. We proposed different classifiers in our work, Resnet-101, Resnet-18, and Densenet-201, etc as Cnn and multiple handcrafted features like LBP, HOG, and MPT for more accurate classification of breast cancer images. Deep learning extract and organizes features from data. We have organized and prepared a competitive comparison of these different implementations by evaluating their accuracy using deep learning and machine learning techniques. We also organized competitive results for handcrafted feature extractors and matched CNN and handcrafted features extractor accuracies with recent work done. We have achieved some enormous results using these different techniques. We brought in light that how deep networks and CNN are taking the place of handcrafted feature extractors in different image classifications.