. 2023 Sep 10;7(4):387–432. doi: 10.1007/s41666-023-00144-3

Table 5.

US studies summarized

Pre-processing technique	Pre-trained model used	Novel technique	Performance	Ref., year
Data augmentation	DarkNet-53	CNN Optimized CNN (RDE & RGW optimizer)	Acc 99.1 $%$	[101], 2022
Oversampling, data augmentation	ResNet-50; ResNet-101	Dynamic U-Net with a ResNet-50 encoder backbone.	Acc: classifier A (normal vs abnormal) 96 $%$ & classifier B (benign vs abnormal) 85 $%$	[88], 2022
Image processing in OpenCV & scikit-image (Python)	EficientNetB2, Inception-V3, ResNet-50	Multistage transfer learning (MSTL). optimizers: Adam, Adagrad, and stochastic gradient descent (SGD)	Acc: Mendeley dataset 99 $%$ & MT-Small-Dataset 98.7 $%$	[98], 2022
Basic geometric augmentation	–	Gaussian Dropout Based Stacked Ensemble CNN model with meta-learners	Acc 92.15%, F1-score 92.21 $%$ , precision 92.26 $%$ , recall 92.17 $%$ .	[90], 2021
ROI extraction, image resolution adjustment, data normalization, data augmentation.	Inception-V3	Fine-tuned Inception-v3 architecture	Acc 98.19 $%$	[91], 2021
–	–	Shape-Adaptive Convolutional (SAC) Operator with K-NN & Self-attention coefficient $+$ U-Net with VGG-16 & ResNet-101.	ResNet-101 $+$ mean IoU 82.15 $%$ (multi-object segmentation) and IoU 77.9 $%$ & 72.12 $%$ (Public BUSI).	[104], 2021
Image resized & normalized	ResNet-101 pre-trained on RGB images.	Novel transfer learning technique based on deep representation scaling (DRS) layers.	AUC: 0.955, acc: 0.915	[118], 2021
Focal loss strategy, data augmentation, rotation, horizontal or vertical flipping, random cropping, random channel shifts.	−	ResNet-50	Test cohort A’s acc: 80.07 $%$ to 97.02 $%$ , AUC: 0.87, PPV: 93.29 $%$ , MCC: 0.59. Test cohort B’s acc: 87.94 $%$ to 98.83 $%$ , AUC: 0.83, PPV: 88.21 $%$ , MCC: 0.79.	[92], 2021
−	ImageNet-based pre-trained weights.	DNN; deepest layers were fine-tuned by minimizing the focal loss.	AUC: 0.70 ((95 $%$ CI,0.63 $-$ 0.77; on 354 TMA samples) & 0.67 (95 $%$ CI,0.62 $-$ 0.71; on 712 whole-slide image).	[114], 2021
Enhanced by fuzzy preprocessing.	FCN-AlexNet, UNet, SegNet-VGG16, SegNetVGG19, DeepLabV3 $+$ (ResNet-18, ResNet-50, MobileNet-V2, Xception).	A scheme based on combining fuzzy logic (FL) and deep learning (DL).	Global acc: 95.45 $%$ , mean IoU: 78.70 $%$ , mean Boundary F1: 68.08 $%$ .	[106], 2021
DL-based data augmentation & online augmentation.	Pre-trained AlexNet & ResNet	Fine-tuned ensemble CNN	Acc: 90 $%$	[109], 2021
Coordinate marking, image cutting, mark removal.	Pretrained Xception CNN	Optimized deep learning model (DLM)	For DLM, acc: 89.7 $%$ , SN: 91.3 $%$ , SP: 86.9 $%$ , AUC: 0.96. For DLM in BI-RADS, acc: 92.86 $%$ . false negative rate 10.4 $%$ .	[94], 2021
Normalizing image stain-color	Pre-trained VGG-19	Block-wise fine-tuned VGG-19 model with softmax classifier on top.	Acc: 94.05 $%$ to 98.13 $%$	[110], 2021
Augmentation: flipping, rotation, gaussian blur, scalar multiplication.	Pre-trained on the MS COCO dataset	Deep learning-based computer-aided prediction (CAP) system. Mask R-CNN, DenseNet-121	Acc: 81.05 $%$ , SN: 81.36 $%$ , SP: 80.85 $%$ , AUC: 0.8054.	[93], 2021
Data augmentation, rotation	Inception-v3	Modified Inception-v3	AUC: 0.9468, SN: 0.886, Specificity: 0.876.	[95], 2020
Data augmentation: random rotation, random shear, random zoom.	−	DenseNet	Raining/testing cohorts AUCs: 0.957/0.912 (combined region), 0.944/0.775 (peritumoral region), (0.937/0.748 (intratumoral region).	[100], 2020
Data augmentation: random geometric image transformations, flipping, rotation, scaling, shifting, resizing	–	Inception-V3, Inception-ResNet-V2, ResNet-101	Inception-V3’s AUC: 0.89 (95 $%$ CI: 0.83, 0.95), SN: 85 $%$ (35 of 41 images; 95 $%$ CI: 70 $%$ , 94 $%$ ), SP: 73 $%$ (29 of 40 images; 95 $%$ CI: 56 $%$ , 85 $%$ )	[96], 2020
Data augmentation: flipping, translation, scaling, and rotation technique.	VGG16, VGG19, ResNet-50	Finetuned CNN	VGG16 with linear SVM’s patch-based accuracies: (93.97 $%$ for 40 $\times$ -, 92.92 $%$ for 100 $\times$ -, 91.23 $%$ for 200 $\times$ -, 91.79 $%$ for 400 $\times$ -); patient-based accuracies: (93.25 $%$ for 40 $\times$ -, 91.87 $%$ for 100 $\times$ -, 91.5 $%$ for 200 $\times$ -,92.31 $%$ for 400 $\times$ -).	[108], 2020
.jpeg conversion, trimmed, resized	−	GoogLeNet CNN	SN: 0.958, SP: 0.875, acc: 0.925, AUC: 0.913.	[117], 2019
Data augmentation: used ROI-CNN & G-CNN model	−	Two-stage grading. ROI-CNN, G-CNN	Acc = 0.998	[97], 2019
Data augmentation	VGG16 CNN	Fine-tuned deep learning parameters	Acc: 0.973, AUC: 0.98	[115], 2019