A Novel deep neural network-based emotion analysis system for automatic detection of mild cognitive impairment in the elderly

Fei, Zixiang; Yang, Erfu; Yu, Leijian; Li, Xia; Zhou, Huiyu; Zhou, Wenjun

doi:10.1016/j.neucom.2021.10.038

Cited by 25 publications

(11 citation statements)

References 24 publications

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“…The model achieved 88% accuracy. The use of AI to diagnose and determine the prognosis of dementia was explored in three studies [ 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…It is thought that, in this phase, it is possible to intervene and slow the progression versus overt dementia during this stage. In this systematic review, four studies employed ML models to detect MCI [ 42 , 43 , 58 ]. An SVM model was the most incorporated algorithm in the detection of MCI and produced accuracy ranging from 73% to 91% [ 56 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

“…Two studies were associated with both ML and DL classifiers [ 38 , 64 ]. A model using SVM and a second using a combination of MobileNet and Block 11 addition and SVM were noted [ 42 ]. In terms of accuracy, the combined model had the highest accuracy of 88.7%, while the SVM model had the lowest accuracy of 73.3%.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

et al. 2022

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Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.

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“…The model achieved 88% accuracy. The use of AI to diagnose and determine the prognosis of dementia was explored in three studies [ 42 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

et al. 2022

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“…Fie et al introduced a novel deep neural network-based system for the early detection of cognitive impairment by analyzing the evolution of facial emotions in response to video stimuli. The system incorporates a facial expression recognition algorithm using layers from MobileNet and a Support Vector Machine (SVM), demonstrating satisfactory performances across three datasets like the KDEF dataset, Chinese Adults Dataset, and Chinese Elderly People Dataset [68]. A significant amount of work has focused on employing transfer learning techniques with CNN models such as AlexNet [69], SqueezeNet [70], and VGG19, evaluating their efficacy on benchmark datasets including FER2013, JAFFE, KDEF, CK+, SFEW [71], and KMU-FED.…”

Section: Badrulhisham Et Al Focused On Real-time Fer Employing Mobilenetmentioning

confidence: 99%

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Chowdhury,

Liu,

Ramanna

2024

Algorithms

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Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

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“…Recently, deep learning-based analysis of sentiments has achieved great success in different areas such as audiovisual [13], image [14], and sequential data [15] processing. While increasing the training data for traditional machine learning techniques does not always improve the performance, in deep learning, the success chance increases as the training data diversifies and increases.…”

Section: Natural Language Processing and Sentiment Analysismentioning

confidence: 99%

Deep learning-based user experience evaluation in distance learning

et al. 2023

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The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals’ financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users’ emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

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A Novel deep neural network-based emotion analysis system for automatic detection of mild cognitive impairment in the elderly

Cited by 25 publications

References 24 publications

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Deep learning-based user experience evaluation in distance learning

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