Abnormality Diagnosis in Mammograms by Transfer Learning Based on ResNet18

Xiang Yu, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

Breast cancer is one of the common cancers threatening the health of women while the incident rate of it is quite low in men to contribute to a major killer of men. Early syndromes of breast cancer including micro-calcification, mass, and distortion in mammography images can be very helpful for radiologists to make diagnosis of the cancer at early stage, which means the cancer can be treated or even be cured timely and thus make early diagnosis important. To assist radiologists with diagnosis, we set up a computer-aided diagnosis system to make diagnosis decision of breast cancer in this paper. We acquired regions of interests in mammographic images from public database, and labeled regions containing micro-calcification or mass as abnormality while regions without such abnormalities as normality. By transferring the state-of-the-art networks into our quest, we found that ResNet18 performed best and achieved mean accuracy of 95.91%.

Original languageEnglish
Pages (from-to)219-230
Number of pages12
JournalFundamenta Informaticae
Volume168
Issue number2-4
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Abnormality
  • Diagnosis system
  • Transfer learning

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