Journal: Cancers

Article Title: Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

doi: 10.3390/cancers15123139

Figure Lengend Snippet: This table shows deep learning models used in the studies stated in Table 1 (2017–February 2023), hyperparameters, loss function, activation function, limitations, and performance metrics.

Article Snippet: 58. , Yap et al. [ ] , Detection of breast lesions , A Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. , Iteration time t was 50, input patches are sized at 28 × 28, Patch based LeNet: Root Mean Square Propagation (RMSProp) was used, a learning rate of 0.01, 60 epochs, the dropout rate of 0.33, U-Net: Adam optimizer, a learning rate of 0.0001, 300 epochs, FCN-AlexNet: Stochastic gradient descent, a learning rate of 0.001, 60 epochs, a dropout rate of 33% , Patch-based LeNet: Multinomial logistic loss , ReLU and Softmax , They need a time-consuming training process and images that are normal. , Transfer Learning FCN-AlexNet performed best, True Positive Fraction 0.98 for dataset A, 0.92 for dataset B.

Techniques: Activation Assay, Labeling, Imaging, Expressing, Blocking Assay, Diagnostic Assay, Generated, Modification, Plasmid Preparation, Histopathology, Selection