Enhanced deep learning algorithm development to detect pain intensity from facial expression images

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

doi:10.1016/j.eswa.2020.113305

Cited by 120 publications

(70 citation statements)

References 11 publications

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“…ML, already has a rich history in biology [99], [100] and chemistry [101], and it has recently gained prominence in the field of solid state materials science. Presently, DL models in ML are effectively used in imaging for classification, detection [102], segmentation [103] and prepossessing. The most famous and commonly employed DL architecture in the selected 65 studies is CNN, which is used in 64 cases, while DBN is implemented once.…”

Section: Discussion and Observationsmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

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Section: Discussion and Observationsmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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“…In a real-life situation, data collection for images can be captured in various conditions, such as different directions, locations, sizes, and visibility. Thus in such raw images, the conventional pre-processing technique such as standardization, cutting, and centralization enhances images' recognition during any experimental period [29]. • Feature extraction: The primary step is to extract facial features from the image or video input [28], [30].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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“…Therefore, we can find these patterns through a huge amount of shapelets through brain signal data. Shapelets identify maximally discriminative segment of the time-series data [28,31]. Shapelet is used for high prediction accuracy has the advantage of being able to find the subsequences that best differentiate between classes [29,32].…”

Section: B Pattern Extraction From Time-series Using Shapeletsmentioning

confidence: 99%

Time-Series Data Classification and Analysis Associated With Machine Learning Algorithms for Cognitive Perception and Phenomenon

Jeong

2020

IEEE Access

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Enhanced deep learning algorithm development to detect pain intensity from facial expression images

Cited by 120 publications

References 11 publications

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Time-Series Data Classification and Analysis Associated With Machine Learning Algorithms for Cognitive Perception and Phenomenon

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