Detection and classification of masses in mammographic images in a multi-kernel approach

Highlights • We propose a method to detect and classify mammographic lesions using the regions of interest of images. • We use using multi-resolution wavelets and Zernike moments as extract feature extractor image stage. • We can combine both texture and shape features, which can be applied both to...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2016-10, Vol.134, p.11-29
Main Authors: de Lima, Sidney M. L, da Silva-Filho, Abel G, dos Santos, Wellington Pinheiro
Format: Article
Language:English
Subjects:
Breast cancer
Breast Neoplasms - classification
Breast Neoplasms - diagnostic imaging
Extreme learning machines
Female
Humans
Internal Medicine
Mammography
Multi-resolution wavelets
Neural Networks (Computer)
Other
Support vector machines
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Summary:Highlights • We propose a method to detect and classify mammographic lesions using the regions of interest of images. • We use using multi-resolution wavelets and Zernike moments as extract feature extractor image stage. • We can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. • Considering the ratio between accuracy and training time, our proposal proved to be 50 times superior to state-of-the-art approaches. • As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2016.04.029