Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 9 Sayı: 4, 325 - 330, 31.12.2023

Öz

Kaynakça

  • [1]. Weitz, K., Hassan, T., Schmid, U., & Garbas, J. U. (2019). Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods. tm-Technisches Messen, 86(7-8); 404-412. DOI: 10.1515/teme-2019-0024.
  • [2]. Fontaine, D., Vielzeuf, V., Genestier, P., Limeux, P., Santucci‐Sivilotto, S., Mory, E., ... & DEFI study group. (2022). Artificial intelligence to evaluate postoperative pain based on facial expression recognition. European Journal of Pain, 26(6); 1282-1291. DOI: 10.1002/ejp.1948.
  • [3]. Hasan, M. K., Ahsan, G. M. T., Ahamed, S. I., Love, R., & Salim, R. (2016). Pain level detection from facial image captured by smartphone. Journal of Information Processing, 24(4); 598-608. DOI: 10.2197/ipsjjip.24.598.
  • [4]. Barua, P. D., Baygin, N., Dogan, S., Baygin, M., Arunkumar, N., Fujita, H., ... & Acharya, U. R. (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); 17297. DOI: 10.1038/s41598-022-21380-4.
  • [5]. Çelik, B., & Çelik, M. E. (2023). Root Dilaceration Using Deep Learning: A Diagnostic Approach. Applied Sciences, 13(14); 8260. DOI: 10.3390/app13148260.
  • [6]. Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022). Medical image segmentation using deep learning: A survey. IET Image Processing, 16(5); 1243-1267. DOI: 10.1049/ipr2.12419.
  • [7]. Çelik, B., & Çelik, M. E. (2022). Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofacial Radiology, 51(8); 20220244. DOI: 10.1259/dmfr.20220244.
  • [8]. Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011, March). Painful data: The UNBC-McMaster shoulder pain expression archive database. In 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG) (pp. 57-64). IEEE. DOI: 10.1109/FG.2011.5771462.
  • [9]. Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F., & Wang, H. (2020). Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications, 149; 113305. DOI: 10.1016/j.eswa.2020.113305.
  • [10]. Nguyen, D. C., Pham, Q. V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., ... & Hwang, W. J. (2022). Federated learning for smart healthcare: A survey. ACM Computing Surveys (CSUR), 55(3); 1-37. DOI: 10.48550/arXiv.2111.08834.
  • [11]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). DOI: 10.48550/arXiv.1512.03385.
  • [12]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. DOI: 10.48550/arXiv.1409.1556.
  • [13]. Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR. DOI: 10.48550/arXiv.1905.11946.
  • [14]. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). DOI: 10.48550/arXiv.1608.06993.
  • [15]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). DOI: 10.48550/arXiv.1409.4842.
  • [16]. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). DOI: 10.1109/CVPR.2017.195.
  • [17]. Erol, T., & Sarikaya, D. (2022). PlutoNet: An Efficient Polyp Segmentation Network with Modified Partial Decoder and Decoder Consistency Training. arXiv preprint arXiv:2204.03652. DOI: 10.48550/arXiv.2204.03652.
  • [18]. Barua, P. D., Baygin, N., Dogan, S., Baygin, M., Arunkumar, N., Fujita, H., ... & Acharya, U. R. (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), 17297. DOI: 10.1038/s41598-022-21380-4.
  • [19]. Hammal, Z., & Cohn, J. F. (2012, October). Automatic detection of pain intensity. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 47-52). DOI: 10.1145/2388676.2388688.

A Novel Deep Learning Model for Pain Intensity Evaluation

Yıl 2023, Cilt: 9 Sayı: 4, 325 - 330, 31.12.2023

Öz

Pain assessment is a critical component of healthcare, influencing effective pain management, individualized care, identification of underlying issues, and patient satisfaction. However, the subjectivity and limitations of self-reported assessments have led to disparities in pain evaluation, particularly in vulnerable populations such as children, the elderly, individuals with cognitive impairments, and those with mental health conditions. Recent advances in technology and artificial intelligence (AI) have paved the way for innovative solutions in pain intensity evaluation.This paper presents a novel deep learning model to automatically classify pain intensity levels and compares them with six state-of-the-art deep learning classification models - ResNet-50, VGG-19, EfficientNet, DenseNets, Inception, and Xception- using the UNBC-McMaster Shoulder Pain Expression Archive Database for training. Transfer learning is employed to optimize model efficiency and minimize the need for extensive labeled data. Model evaluations are conducted based on accuracy, precision, recall, and F1 score. The proposed model, ZNet, showed superior performance with accuracy of 95.4%, precision and recall of 64.4% and 63.4%, respectively, and F1-score of 63.7%. Furthermore, this study addresses the challenge of accurately evaluating pain intensity in patients who cannot communicate verbally or face language barriers. By harnessing AI technology and facial expression analysis methods, we aim to provide an objective, reliable, and precise pain assessment methodology. Automated artificial based solutions enhance the reliability of pain evaluations, and holds promise for improving decision-making in pain management and treatment processes, ultimately enhancing patients' quality of life.

Kaynakça

  • [1]. Weitz, K., Hassan, T., Schmid, U., & Garbas, J. U. (2019). Deep-learned faces of pain and emotions: Elucidating the differences of facial expressions with the help of explainable AI methods. tm-Technisches Messen, 86(7-8); 404-412. DOI: 10.1515/teme-2019-0024.
  • [2]. Fontaine, D., Vielzeuf, V., Genestier, P., Limeux, P., Santucci‐Sivilotto, S., Mory, E., ... & DEFI study group. (2022). Artificial intelligence to evaluate postoperative pain based on facial expression recognition. European Journal of Pain, 26(6); 1282-1291. DOI: 10.1002/ejp.1948.
  • [3]. Hasan, M. K., Ahsan, G. M. T., Ahamed, S. I., Love, R., & Salim, R. (2016). Pain level detection from facial image captured by smartphone. Journal of Information Processing, 24(4); 598-608. DOI: 10.2197/ipsjjip.24.598.
  • [4]. Barua, P. D., Baygin, N., Dogan, S., Baygin, M., Arunkumar, N., Fujita, H., ... & Acharya, U. R. (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); 17297. DOI: 10.1038/s41598-022-21380-4.
  • [5]. Çelik, B., & Çelik, M. E. (2023). Root Dilaceration Using Deep Learning: A Diagnostic Approach. Applied Sciences, 13(14); 8260. DOI: 10.3390/app13148260.
  • [6]. Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022). Medical image segmentation using deep learning: A survey. IET Image Processing, 16(5); 1243-1267. DOI: 10.1049/ipr2.12419.
  • [7]. Çelik, B., & Çelik, M. E. (2022). Automated detection of dental restorations using deep learning on panoramic radiographs. Dentomaxillofacial Radiology, 51(8); 20220244. DOI: 10.1259/dmfr.20220244.
  • [8]. Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P. E., & Matthews, I. (2011, March). Painful data: The UNBC-McMaster shoulder pain expression archive database. In 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG) (pp. 57-64). IEEE. DOI: 10.1109/FG.2011.5771462.
  • [9]. Bargshady, G., Zhou, X., Deo, R. C., Soar, J., Whittaker, F., & Wang, H. (2020). Enhanced deep learning algorithm development to detect pain intensity from facial expression images. Expert Systems with Applications, 149; 113305. DOI: 10.1016/j.eswa.2020.113305.
  • [10]. Nguyen, D. C., Pham, Q. V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., ... & Hwang, W. J. (2022). Federated learning for smart healthcare: A survey. ACM Computing Surveys (CSUR), 55(3); 1-37. DOI: 10.48550/arXiv.2111.08834.
  • [11]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). DOI: 10.48550/arXiv.1512.03385.
  • [12]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. DOI: 10.48550/arXiv.1409.1556.
  • [13]. Tan, M., & Le, Q. (2019, May). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR. DOI: 10.48550/arXiv.1905.11946.
  • [14]. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). DOI: 10.48550/arXiv.1608.06993.
  • [15]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). DOI: 10.48550/arXiv.1409.4842.
  • [16]. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). DOI: 10.1109/CVPR.2017.195.
  • [17]. Erol, T., & Sarikaya, D. (2022). PlutoNet: An Efficient Polyp Segmentation Network with Modified Partial Decoder and Decoder Consistency Training. arXiv preprint arXiv:2204.03652. DOI: 10.48550/arXiv.2204.03652.
  • [18]. Barua, P. D., Baygin, N., Dogan, S., Baygin, M., Arunkumar, N., Fujita, H., ... & Acharya, U. R. (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), 17297. DOI: 10.1038/s41598-022-21380-4.
  • [19]. Hammal, Z., & Cohn, J. F. (2012, October). Automatic detection of pain intensity. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 47-52). DOI: 10.1145/2388676.2388688.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Cerrahi (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Mahmut Emin Çelik 0000-0002-1766-5514

Erken Görünüm Tarihi 20 Ekim 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 7 Ekim 2023
Kabul Tarihi 20 Ekim 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 9 Sayı: 4

Kaynak Göster

APA Çelik, M. E. (2023). A Novel Deep Learning Model for Pain Intensity Evaluation. International Journal of Computational and Experimental Science and Engineering, 9(4), 325-330.