| Full text | |
| Author(s): |
Oliveira, Guilherme C.
;
Ngo, Quoc C.
;
Passos, Leandro A.
;
Papa, Joao P.
;
Jodas, Danilo S.
;
Kumar, Dinesh
Total Authors: 6
|
| Document type: | Journal article |
| Source: | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE; v. 240, p. 8-pg., 2023-10-01. |
| Abstract | |
Background and Objective: This paper presents a method for the computerized detection of hypomimia in people with Parkinson's disease (PD). It overcomes the difficulty of the small and unbalanced size of available datasets. Methods: A public dataset consisting of features of the video recordings of people with PD with four facial expressions was used. Synthetic data was generated using a Conditional Generative Adversarial Network (CGAN) for training augmentation. After training the model, Test-Time Augmentation was performed. The classification was conducted using the original test set to prevent bias in the results. Results: The employment of CGAN followed by Test-Time Augmentation led to an accuracy of classification of the videos of 83%, specificity of 82%, and sensitivity of 85% in the test set that the prevalence of PD was around 7% and where real data was used for testing. This is a significant improvement compared with other similar studies. The results show that while the technique was able to detect people with PD, there were a number of false positives. Hence this is suitable for applications such as population screening or assisting clinicians, but at this stage is not suitable for diagnosis. Conclusions: This work has the potential for assisting neurologists to perform online diagnose and monitoring their patients. However, it is essential to test this for different ethnicity and to test its repeatability. (AU) | |
| FAPESP's process: | 19/00585-5 - Evolutionary Generative Adversarial Networks applied to computer-assisted diabetic retinopathy diagnosis |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks |
| Grantee: | Daniel Carlos Guimarães Pedronette |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction? |
| Grantee: | Alexandre Xavier Falcão |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 19/02205-5 - Adversarial learning in natural language processing |
| Grantee: | Gustavo Henrique de Rosa |
| Support Opportunities: | Scholarships in Brazil - Doctorate |