Nowadays, the diagnosis of numerous diseases is facilitated by medical imaging. In that context, the identification of brain lesions presented as White Matter Hyperintensities (WHMs) and their related diseases is essential to have a correct diagnosis. Machine- and deep learning (subfields within artificial intelligence) could support the diagnosis (especially in complex medical images) by leveraging the structure and regularities within the imaging data. This project presents a technique for the classification of WHMs concerning ischemia and demyelination through the analysis of the region of interest (ROI) features of magnetic resonance images. To do that, we analyzed radiomic features using a combination of principal component analysis (PCA) and support vector machine (SVM) classification. Next, we used a transfer learning fine-tuned ResNet18 model to more thoroughly analyze and classify lesioned ROIs. For that, we used patient data alone and additional synthetic data (generated using spectral generative adversarial networks -SNGAN). The results show an accuracy mean value of 0.96 without data augmentation; while we had a value of 0.54 using synthetic data, a similar value was acquired with radiomics-informed SVM classification (0.56). These findings constitute a starting point for future projects exploring different ways of informing and fine-tuning artificial intelligence models to detect, classify, and segment MRI pathologies characterized by small lesions.