Breast cancer classification of image using convolutional neural network

Convolutional Neural Network (CNN) has been set up as an intense class of models for image acknowledgment issues. CNN is a deep learning model which extracts the feature of an image and use these feature to classify an image. Other classification algorithm needs to extract the feature of an image using feature extraction algorithm like Gray Level Co-occurrence Matrix. Convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied to recognizing image. It is also widely used in video recognition, image classification, recommender systems, natural language processing and speech recognition. In this paper, a dataset of 7909 breast cancer histopathology images acquired on 82 patients are taken. These images are of two different classes benign and malignant. We extract the patches of the image to train the network and finally we give the image as an input to classify the image. Performance of CNN is much better when compared to other reported results on MNSIT dataset using other classification algorithm for classifying an image.