J Shanghai Jiaotong Univ Sci ›› 2022, Vol. 27 ›› Issue (4): 473-484.doi: 10.1007/s12204-021-2398-x

• Medicine-Engineering Interdisciplinary Research • Previous Articles     Next Articles

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)   

  1. (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)
  • Received:2021-08-03 Online:2022-07-28 Published:2022-08-11

Abstract: 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.

Key words: breast cancer, BreakHis, transfer learning, convolutional neural network, feature concatenation

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