Affiliations: [a] Department of Informatics, University of Leicester, LE1 7RH, UK. [email protected] | [b] School of Architecture, Building and Civil engineering, Loughborough University, LE11 3TU, UK. [email protected]
Correspondence:
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Address for correspondence: School of Architecture, Building and Civil engineering, Loughborough University, LE11 3TU, UK
Note: [†] Xiang Yu holds a China Scholarship Council studentship with the University of Leicester
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%.
Keywords: Abnormality, Diagnosis system, Transfer learning
