  This paper presents and validates a novel lung nodule classification
algorithm that uses multifractal features found in X-ray images. The proposed
method includes a pre-processing step where two enhancement techniques are
applied: histogram equalization and a combination of wavelet decomposition and
morphological operations. As a novelty, multifractal features using wavelet
leader based formalism are used with Support Vector Machine classifier; other
classical texture features were also included. Best results were obtained when
using multifractal features in combination with classical texture features,
with a maximum ROC AUC of 75\%. The results show improvements when using data
augmentation technique, and parameter optimization. The proposed method proved
to be more efficient and accurate than Modulus Maxima Wavelet Formalism in both
computational cost and accuracy when compared in a similar experimental set up.
