Computer Aided Diagnosis System to Detect Breast Cancer Pathological Lesions

被引：0

作者：

Lopez, Yosvany ^{[1
]}

Novoa, Andra ^{[1
]}

Guevara, Miguel A. ^{[1
,2
]}

Quintana, Nicolas ^{[1
]}

Silva, Augusto ^{[2
]}

机构：

[1] Ciego Avila Univ, Ctr Adv Comp Sci Technol, Ciego De Avila, Cuba

[2] Univ Aveiro, IEETA, Aveiro, Portugal

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS | 2008年 / 5197卷

关键词：

Breast cancer; pathological lesion; mammography images; CAD system; artificial neural networks;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Breast cancer is one of the most frequent forms of women's cancer over the world. Studies of the World Health Organization (WHO) reported 1,151,298 cases in 2002. A reliable Computer-Aided-Diagnosis (CAD) system for automated detection/classification of pathological lesions is very useful and helpful, providing it valuable "second opinion" to medical personnel. In this work, we describe it new CAD system to diagnose six mammooraphy pathological lesions classes (calcifications. well-defined/circumscribed masses, spiculated masses. ill-defined masses, architectural distortions and asymmetries) its benign or malionant tissues. Two different Artificial Neural Networks models: Feedforward Backpropagation and Generalized Regression were tested statistically with a precision of 94.0% and 80.0% of true positives, respectively. This CAD system was validated successfully on the MiniMammographic linage Analysis Society (MiniMIAS) database, with a dataset formed by 100 images. The CAD system performance shows similar or better classification results compared with others available methods,

引用

页码：453 / +

页数：3

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