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: 37. , Cao et al. [ ] , Comparison of the performances of deep learning models for breast lesion detection and classification methods , AlexNet, ZFNet, VGG, ResNet, GoogLeNet, DenseNet, Fast Region-based convolutional neural networks (R-CNN), Faster R-CNN, Spatial Pyramid Pooling Net, You Only Look Once (YOLO), YOLO version 3 (YOLOv3), and Single Shot MultiBox Detector (SSD) , Image input size was different which was resized to 256 × 256 pixels, epoch: 2000 , Not specified , Softmax, bounding-box regression , 1. SSD300 + ZFNet is better than SSD300 + VGG16 under the benign, but worse under the malignant lesions, due to model complexity, 2. VGG16 reaches overfitting for benign lesions, 3. AlexNet, ZFNet, and VGG16 perform poorly for full images and LROI, while learning from scratch, due to the dimensionality problem, leading to over-fitting. , Transfer learning from the modified ImageNet produces higher accuracy than random initialization, and DenseNet provides the best result..

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