دورية أكاديمية
Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images
| العنوان: | Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images |
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| المؤلفون: | Prabal Datta Barua, Nursena Baygin, Sengul Dogan, Mehmet Baygin, N. Arunkumar, Hamido Fujita, Turker Tuncer, Ru-San Tan, Elizabeth Palmer, Muhammad Mokhzaini Bin Azizan, Nahrizul Adib Kadri, U. Rajendra Acharya |
| المصدر: | Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-022-21380-4 |
| URL الوصول: | https://doaj.org/article/ea13b91f8a8f4b7294176718587b6c62 |
| رقم الأكسشن: | edsdoj.13b91f8a8f4b7294176718587b6c62 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-022-21380-4 |