  Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used
in several domains, which include computer-assisted medical diagnoses. In this
work, we are interested in developing tools for the automatic identification of
Parkinson's disease using machine learning and the concept of BoVW. The
proposed approach concerns a hierarchical-based learning technique to design
visual dictionaries through the Deep Optimum-Path Forest classifier. The
proposed method was evaluated in six datasets derived from data collected from
individuals when performing handwriting exams. Experimental results showed the
potential of the technique, with robust achievements.
