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Breast Pathological Image Classification Based on VGG16 Feature Concatenation
LIU Min (刘 敏), YI Ming (易 鸣), WU Minghu∗ (武明虎), WANG Juan (王 娟), HE Yu (何 宇)
J Shanghai Jiaotong Univ Sci
2022, 27 (4):
473-484.
DOI: 10.1007/s12204-021-2398-x
Breast cancer is one of the malignancies that endanger women’s health all over the world. Considering
that there is some noise and edge blurring in breast pathological images, it is easier to extract shallow features
of noise and redundant information when VGG16 network is used, which is affected by its relative shallow depth
and small convolution kernel. To improve the pathological diagnosis of breast cancers, we propose a classification
method for benign and malignant tumors in the breast pathological images which is based on feature concatenation
of VGG16 network. First, in order to improve the problems of small dataset size and unbalanced data samples, the
original BreakHis dataset is processed by data augmentation technologies, such as geometric transformation and
color enhancement. Then, to reduce noise and edge blurring in breast pathological images, we perform bilateral
filtering and denoising on the original dataset and sharpen the edge features by Sobel operator, which makes the
extraction of shallow features by VGG16 model more accurate. Based on transfer learning, the network model
trained with the expanded dataset is called VGG16-1, and another model trained with the image denoising and
sharpening and mixed with the original dataset is called VGG16-2. The features extracted by VGG16-1 and
VGG16-2 are concatenated, and then classified by support vector machine. The final experimental results show
that the average accuracy is 98.44%, 98.89%, 98.30% and 97.47%, respectively, when the proposed method is
tested with the breast pathological images of 40×, 100×, 200× and 400× on BreakHis dataset.
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