  Reliable classification of benign and malignant lesions in breast ultrasound
images can provide an effective and relatively low cost method for early
diagnosis of breast cancer. The accuracy of the diagnosis is however highly
dependent on the quality of the ultrasound systems and the experience of the
users (radiologists). The leverage in deep convolutional neural network
approaches provided solutions in efficient analysis of breast ultrasound
images. In this study, we proposed a new framework for classification of breast
cancer lesions by use of an attention module in modified VGG16 architecture. We
also proposed new ensembled loss function which is the combination of binary
cross-entropy and logarithm of the hyperbolic cosine loss to improve the model
discrepancy between classified lesions and its labels. Networks trained from
pretrained ImageNet weights, and subsequently fine-tuned with ultrasound
datasets. The proposed model in this study outperformed other modified VGG16
architectures with the accuracy of 93% and also the results are competitive
with other state of the art frameworks for classification of breast cancer
lesions. In this study, we employed transfer learning approaches with the
pre-trained VGG16 architecture. Different CNN models for classification task
were trained to predict benign or malignant lesions in breast ultrasound
images. Our Experimental results show that the choice of loss function is
highly important in classification task and by adding an attention block we
could empower the performance our model.
