Hierarchical Learning Using Deep Optimum-Path Forest

TL;DR

This paper introduces a hierarchical learning method using Deep Optimum-Path Forest for automatic Parkinson's disease identification from handwriting data, demonstrating promising results across multiple datasets.

Contribution

It proposes a novel hierarchical learning approach with Deep Optimum-Path Forest for visual dictionary creation in Parkinson's diagnosis, advancing machine learning applications in medical diagnostics.

Findings

Robust classification performance across six datasets

Effective use of hierarchical learning with Deep Optimum-Path Forest

Potential for aiding medical diagnosis of Parkinson's disease

Abstract

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.