Barua, Prabal Datta and Baygin, Nursena and Dogan, Sengul and Baygin, Mehmet and Arunkumar, N. and Fujita, Hamido and Tuncer, Turker and Tan, Ru-San and Palmer, Elizabeth and Bin Azizan, Muhammad Mokhzaini and Kadri, Nahrizul Adib and Acharya, U. Rajendra (2022) Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-21380-4.
Full text not available from this repository.Abstract
Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or ``shutter blinds''. A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Expressions; Disease |
| Subjects: | R Medicine T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 27 Sep 2023 06:51 |
| Last Modified: | 27 Sep 2023 06:51 |
| URI: | http://eprints.um.edu.my/id/eprint/40799 |
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