A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique
Abeer Saber, Mohamed Sakr, Osama M. Abo-Seida, Arabi Keshk, Huiling Chen
Abstract
Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80-20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis- society (MIAS) dataset using a pre-trained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall accuracy, sensitivity, specificity, precision, F-score, and AUC of 98.96%, 97.83%, 99.13%, 97.35%, 97.66%, and 0.995, respectively for 80-20 method and 98.87%, 97.27%, 98.2%, 98.84%, 98.04%, and 0.993 for 10-fold cross-validation method.