Abnormality Diagnosis in Mammograms by Transfer Learning Based on ResNet18

• Autorzy: Yu Xiang , Wang Shui-Hua

Identyfikatory

DOI

10.3233/FI-2019-1829

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

abnormality; diagnosis system; transfer learning

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2019
• Tom: Vol. 168, nr 2-4
• Strony: 219--230
• Opis fizyczny: Bibliogr. 32 poz., fot., rys., tab.

Twórcy

autor

Yu Xiang

• Department of Informatics, University of Leicester, LE1 7RH, UK

autor

Wang Shui-Hua

• School of Architecture, Building and Civil engineering, Loughborough University, LE11 3TU, UK

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).