Chaos theory-based quantification of ROIs for mammogram classification

What is it about?

The paper presents improved method for breast cancer diagnosis. The previous method has been presented in Dhahbi et al. 2015. The suggested improvements regards applying more diagnostic features such as wavelet packet decomposition, Hilbert matrix, fractal texture features, etc. Moreover we investigated several classifiers such as Random Forest, Support Vector Machine and Decision Tree. In this paper larger database (number of images/trials) has been used and reached better accuracy.

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