Ensemble neural network approach detecting pain intensity from facial expressions

Bargshady, Ghazal; Zhou, Xujuan; Deo, Ravinesh C.; Soar, Jeffrey; Whittaker, Frank; Wang, Hua

doi:10.1016/j.artmed.2020.101954

Cited by 44 publications

(35 citation statements)

References 49 publications

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“…It occurs when the number of instances for different classes are significantly out of proportion. The minority classes with fewer instances usually contain the essential information, which has been observed in broad application areas, such as medical diagnosis [ 1 , 2 , 3 , 4 , 5 , 6 ], sentiment or image classification [ 7 , 8 ], fault identification [ 9 , 10 ], etc. Many typical classifiers may generate unsatisfactory results due to a concentration on global accuracy while ignoring the identification performance for minority samples.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…A various of machine learning/deep learning algorithms have been applied to medical and healthcare domain including cancer research [37], prediction of sepsis [38], chronic pain detection [39], [40], coronary artery disease research [41]- [43].…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Notice of Removal: Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques

et al. 2022

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Public hospitals receive and triage a large volume of medical referrals for otorhinolaryngology annually and it can be a challenge to derive knowledge from them as they are written in unstructured text and may be unavailable in electronic formats. Acquiring knowledge and insights from these referrals are important to public health management and policymakers. Triaging of general practitioner (GP) referrals for ear, nose, and throat (ENT) specialists is a manual process performed by experienced clinicians, but it is time-consuming. This paper proposes utilising machine learning and data mining to automate the process of referrals. In this study, an ensemble of machine learning algorithms to perform clinical text mining against the unstructured referral text in order to derive the relationship among the discovered medical terms was proposed and implemented. A set of comprehensive term sets' association rules which describe the entire referral dataset's characteristics was obtained from the association rule mining experiments. The neural network-based text classification model that can classify referrals with high accuracy was developed, tested and reported in this paper.

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“…The applications in facial diagnostics include predictions for classification of skin pathologies [ 12 – 14 ] or dysmorphic features [ 15 – 19 ], attractiveness [ 4 , 20 – 22 ], perceived age [ 4 , 20 , 23 ], or gender [ 24 ]. ANN have also been trained to detect health-related patterns and traits in patients, such as pain [ 25 , 26 ] or stress [ 27 ]. These and many more have the potential to alter the healing arts by providing the following benefits to medico-dental care:…”

Section: Ai Applications In Medico-dental Diagnostics Of the Face: Op...mentioning

confidence: 99%

Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges

et al. 2022

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Objectives This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. Material and methods Narrative literature review. Results Narrative literature review. Conclusion Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients’ facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. Clinical significance While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics.

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Ensemble neural network approach detecting pain intensity from facial expressions

Cited by 44 publications

References 49 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Notice of Removal: Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques

Artificial intelligence in medico-dental diagnostics of the face: a narrative review of opportunities and challenges

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