Fig 1.
The SE-ResNet module architecture.
(A) Basic SE-ResNet module. (B) Bottleneck SE-ResNet module. (C) Small SE-ResNet module.
Table 1.
SE-ResNet architectures for Cifar.
Building modules are shown in brackets, with the numbers of modules stacked. Downsampling is performed by conv3_1, conv4_1, and conv5_1 with a stride of 2.
Table 2.
Experimental results of different SE-ResNet architectures on Cifar.
Fig 2.
The BHCNet-3 architecture for the benign and malignant classification of breast cancer histopathological images.
Fig 3.
Comparison of experimental results of different learning rate schedulers on Cifar-10.
Fig 4.
Gaussian error scheduler with different α and β.
Table 3.
The error rate of different schedulers on Cifar with ResNet-18 and cutout.
Fig 5.
Image samples from the BreaKHis 40× dataset.
(A) Adenosis, (B) Fibroadenoma, (C) Tubular Adenoma, Phyllodes Tumor, (D) Ductal Carcinoma, (E) Lobular Carcinoma, (F) Mucinous Carcinoma, (G) Papillary Carcinoma.
Table 4.
Structure of the BreaKHis dataset.
Fig 6.
The accuracy curve and loss curve and confusion matrix of BHCNet-3 for the binary classification.
The left column is the accuracy curve. The middle column is the loss curve, and the right column is the confusion matrix. From top to bottom are 40X, 100X, 200X and 400X magnification factors.
Table 5.
The accuracies performance of BHCNet-3 for the binary classification.
Table 6.
The evaluation metrics computed from best result of the BHCNet-3 in each magnification factor and compare the result to the previous work.
Fig 7.
(A) Performance comparison of different learning rate schedulers. (B) Training curves of different learning rate scheduler in 100× magnification factor. (C) Confusion matrix for different α (y-axis) and β (x-axis) for the test accuracy.
Fig 8.
The accuracy curve and loss curve and confusion matrix of BHCNet-6 for the multi-classification.
The left column is the accuracy curve. The middle column is the loss curve, and the right column is the confusion matrix. From top to bottom are 40X, 100X, 200X and 400X magnification factors.
Table 7.
The accuracies performance of BHCNet-6 for the multi-classification.
Table 8.
The evaluation metrics computed from best result of the BHCNet-6 in each magnification factor and compare the result to the previous work.