SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Si Yuan Lu, Shui Hua Wang*, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis.

Original languageEnglish
Article number105812
JournalComputers in Biology and Medicine
Volume148
DOIs
Publication statusPublished - Sept 2022
Externally publishedYes

Keywords

  • Breast cancer
  • Computer-aided diagnosis
  • Randomized neural network
  • Randomized vector functional-link
  • ResNet
  • Ultrasound image

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