Ensemble CNNs for Breast Tumor Classification
To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.
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Methods
1x1 Convolution •
Average Pooling •
Batch Normalization •
Concatenated Skip Connection •
Convolution •
Dense Block •
Dense Connections •
DenseNet •
Depthwise Convolution •
Depthwise Separable Convolution •
Dropout •
Global Average Pooling •
Inverted Residual Block •
Kaiming Initialization •
Max Pooling •
Pointwise Convolution •
ReLU •
RMSProp •
Sigmoid Activation •
Softmax •
Squeeze-and-Excitation Block •
Swish
