The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis

2010 ◽  
pp. 173-190 ◽  
Author(s):  
Evangelos Triantaphyllou
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Computer Aided ◽  
Reliability Issue

The Reliability Issue of Computer-Aided Breast Cancer Diagnosis

2000 ◽  
Vol 33 (4) ◽  
pp. 296-313 ◽  
Author(s):  
Boris Kovalerchuk ◽  
Evangelos Triantaphyllou ◽  
James F. Ruiz ◽  
Vetle I. Torvik ◽  
Evgeni Vityaev
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Computer Aided ◽  
Reliability Issue

Improving breast cancer diagnosis with computer-aided diagnosis

1999 ◽  
Vol 6 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Yulei Jiang ◽  
Robert M. Nishikawa ◽  
Robert A. Schmidt ◽  
Charles E. Metz ◽  
Maryellen L. Giger ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Multimodality Computer-Aided Breast Cancer Diagnosis with FFDM and DCE-MRI

2010 ◽  
Vol 17 (9) ◽  
pp. 1158-1167 ◽  
Author(s):  
Yading Yuan ◽  
Maryellen L. Giger ◽  
Hui Li ◽  
Neha Bhooshan ◽  
Charlene A. Sennett
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Dce Mri ◽  
Computer Aided

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS

2014 ◽  
Vol 26 (03) ◽  
pp. 1450033 ◽  
Author(s):  
Maria Rizzi ◽  
Matteo D'Aloia
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Mammographic Screening ◽  
Image Features ◽  
General Validity ◽  
Artificial Neural Network Classifier ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Microcalcification Clusters

Computer aided detection and Diagnosis systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, a computer aided system for breast cancer diagnosis in mammographic images is presented. In particular, the method looks for microcalcification cluster occurrence and makes the diagnosis of the detected abnormality. The procedure first detects microcalcifications having a cluster pattern and then classifies the abnormalities as benign or malignant clusters. The method formulates the differentiation between malignant and benign microcalcification clusters as a supervised learning problem implementing an artificial neural network classifier. As input to the classifier, the procedure uses image features automatically extracted from the detected clusters. The seven features used are related both to the distribution of microcalcifications within cluster and to the uniformity of their shape. The performance of the implemented system is evaluated taking into account the accuracy of classifying clusters. The obtained results make this method able to operate as a "second opinion" helping radiologists during the routine clinical practice. Moreover, the implemented method has a general validity and can be used to detect and to classify microcalcification clusters independently from the acquisition equipment adopted during the mammographic screening.

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