A Study on the Possible Diagnosis of Parkinson’s Disease on the Basis of Facial Image Analysis

Abstract

One of the symptoms of Parkinson’s disease is the occurrence of problems with the expression of emotions on the face, called facial masking, facial bradykinesia or hypomimia. Recent medical studies show that this symptom can be used in the diagnosis of this disease. In the presented study, the authors, on the basis of their own research, try to answer the question of whether it is possible to build an automatic Parkinson’s disease recognition system based on the face image. The research used image recordings in the field of visible light and infrared. The material for the study consisted of registrations in a group of patients with Parkinson’s disease and a group of healthy patients. The patients were asked to express a neutral facial expression and a smile. In the detection, both geometric and holistic methods based on the use of convolutional network and image fusion were used. The obtained results were assessed quantitatively using statistical measures, including F1score, which was a value of 0.941. The results were compared with a competitive work on the same subject. A novelty of our experiments is that patients with Parkinson’s disease were in the so-called ON phase, in which, due to the action of drugs, the symptoms of the disease are reduced. The results obtained seem to be useful in the process of early diagnosis of this disease, especially in times of remote medical examination.

Keywords

• Parkinson’s disease
• image analysis
• convolutional networks