The use of complex classification algorithms such as deep learning techniques does not allow the researchers to identify the most discriminant features for tumor classification as they lack interpretability. This study aims to develop an algorithm capable of differentiating a set of dermoscopic images depending on whether the tumor is benign or malignant. The priority of this research is to obtain the importance of each extracted feature. This work is focused on the ABCD rule feature analysis and it aims to find the relevance of each feature and its performance in a classification model. A relevant aspect of this study is the use of a heterogeneous database, where the images were uploaded by different sources worldwide. A combination of novel and previously used features are analyzed and their importance is computed by the use of a Gaussian mixture model. After selecting the most discriminant features, a set of classification models was applied to find the best model with the less quantity of features. We found that a total of 65.89% of the features could be omitted with a loss in accuracy, sensibility and specificity equal or lower than 2%. While similar performance measures have been employed in other studies, most results are not comparable, as the databases used were more homogeneous. In the remaining studies, sensitivity values are comparable, with the main difference that the proposed model is interpretable.
Neuro-degenerative diseases can break brain's common output pathways of peripheral nerves and muscles in an individual, inhibiting his ability to perform daily tasks. Brain Computer Interfaces BCI make decoding-encoding of brain signals into control instructions for external devices. This work proposes the use of stacked autoencoders and a softmax layer for classification of visual stimuli from Electrocorticographic (ECoG) signals as an input to the BCI control system. Experimental results show that the proposed method has a good classification performance (average accuracy across subjects 0.95 +/- 0.05), compared to state-of-the-art approaches as Support Vector Machines SVM. Furthermore, the proposed network architecture allows analysis of the weights learned by the classifier making it possible to obtain insights of what signal features the classifier uses to discriminate the visual stimulus.