Hierarchical Learning Using Deep Optimum-Path Forest
This work addresses the need for automated diagnostic tools in medical domains, specifically for Parkinson's disease identification, but appears incremental as it builds on existing BoVW and deep learning methods.
The paper tackled the problem of automatically identifying Parkinson's disease by developing a hierarchical learning technique using Deep Optimum-Path Forest to design visual dictionaries from handwriting exam data, achieving robust results across six datasets.
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.