Breast Cancer Classification in Ultrasound Images using Transfer Learning

Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging

“…In [24] and [26], both the feature extraction (AUC = 0.849) and fine-tuning (AUC = 0.895) approaches were used, and the fine-tuning approach exhibited better performance. These results justify the fact that almost all of the previous studies on transfer learning applied to breast ultrasound [24][25][26][27][28][29] used fine-tuning to achieve superior performance (AUC = 0.895). However, in the performance analysis, the above conclusion does not provide sufficient insights into drawing a clear conclusion, because different studies used different methods (see Section 2.5) in terms of pre-processing, which highly affected performance; others even used different performance analysis metrics [23][24][25][26][27][28][29].…”

“…For example, the last layer in a network that has been trained for classification would be highly specific to that classification task [49]. If the model was trained to classify tumors, one unit would respond only to the images of a specific tumor [23][24][25][26][27][28]. Transferring all layers except the top layer is the most common type of transfer learning [17][18][19][20].…”

“…Transfer learning facilitates the building of a solid machine learning model with comparatively smaller training data because the model is already trained [53]. This is especially valuable in medical image processing because most of the time, data annotating persons are required to create large labeled datasets [24][25][26][27][28][29]. Furthermore, training time is minimized because it can reduce the time required to train a new deep neural network from the beginning in the case of complex target task [48,49].…”

“…The most common pre-training models used for transfer learning in breast ultrasound are the VGG19, VGG16, AlexNet, and InceptionV3 models; VGG is the most common, followed by AlexNet and Inception, which are the least common. A comparison of the different pre-training models is not useful to determine the pre-training model that is better than the others for transfer learning in breast ultrasound [23][24][25][26][27][28][29]. However, one study [26], showed that Inception V3 outperforms VGG19, where the authors evaluated the impact of the ultrasound image reconstruction method on breast lesion classification using a neural transfer learning.…”

“…Shortly after this, Yap et al [25] published their work, which proposed the use of deep neural learning methods for breast cancer detection; they studied three different methods-a patch-based LeNet approach, a U-Net model, and a transfer learning method-with a pre-trained fully convolutional network, AlexNet. Following these works, a large number of articles have been published in the area of applying transfer learning for breast ultrasound imaging [26][27][28][29].…”