Objective: Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. The aim of this work is to develop a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. Two models of deep neural network architectures were used combined to gradient boosted trees classifier. Images were classified in three classes, normal tissue-benign lesions, in situ carcinoma and invasive carcinoma.
Results: Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. The proposed classification allowed us to obtain high degree of precision, a good generalization performance and avoided an eventual overfitting scenario due to the limited size of the data. In addition, we reported high sensitivity for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision.