Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features

TL;DR

This paper presents a machine learning approach to automatically distinguish between central and peripheral facial palsy using facial features, achieving high accuracy and sensitivity on a specialized dataset.

Contribution

It introduces a novel application of machine learning for rapid diagnosis of facial palsy types, with detailed evaluation of algorithms and publicly available code.

Findings

SVM achieved 85.1% accuracy

Naive Bayes achieved 80.7% accuracy

Estimated dataset size of 334 images for 95% sensitivity

Abstract

Central palsy is a form of facial paralysis that requires urgent medical attention and has to be differentiated from other, similar conditions such as peripheral palsy. To aid in fast and accurate diagnosis of this condition, we propose a machine learning approach to automatically classify peripheral and central facial palsy. The Palda dataset is used, which contains 103 peripheral palsy images, 40 central palsy, and 60 healthy people. Experiments are run on five machine learning algorithms. The best performing algorithms were found to be the SVM (total accuracy of 85.1%) and the Gaussian naive Bayes (80.7%). The lowest false negative rate on central palsy was achieved by the naive Bayes approach (80% compared to 70%). This condition could prove to be the most severe, and thus its sensitivity is another good way to compare algorithms. By extrapolation, a dataset size of 334 total pictures is estimated to achieve a central palsy sensitivity of 95%. All code used for these machine learning experiments is freely available online at https://github.com/cvvletter/palsy.