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Kong X, Wang Z, Sun J, Qi X, Qiu Q, Ding X. Facial recognition for disease diagnosis using a deep learning convolutional neural network: a systematic review and meta-analysis. Postgrad Med J 2024:qgae061. [PMID: 39102373 DOI: 10.1093/postmj/qgae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/20/2024] [Accepted: 04/24/2024] [Indexed: 08/07/2024]
Abstract
BACKGROUND With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention. OBJECTIVE This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification. METHODS This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software. RESULTS The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)]. CONCLUSION The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.
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Affiliation(s)
- Xinru Kong
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
- Department of Vertigo Center, Air Force Specialized Medical Center, Beijing 100142, China
| | - Ziyue Wang
- Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 250355, China
| | - Jie Sun
- Rizhao Central Hospital, Rizhao, Shandong 276800, China
| | - Xianghua Qi
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
| | - Qianhui Qiu
- Department of Otolaryngology and Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Cardiovsacular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Science, Guangzhou 510000, China
| | - Xiao Ding
- Department of Neurology II, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, No. 16369, Jingshi Road, Lixia District, Jinan City, Shandong Province 25000, China
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2
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Alsharif N, Al-Adhaileh MH, Al-Yaari M, Farhah N, Khan ZI. Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis. Front Med (Lausanne) 2024; 11:1436646. [PMID: 39099594 PMCID: PMC11294196 DOI: 10.3389/fmed.2024.1436646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 07/05/2024] [Indexed: 08/06/2024] Open
Abstract
Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.
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Affiliation(s)
- Nizar Alsharif
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Computer Engineering and Science, Albaha University, Al Bahah, Saudi Arabia
| | - Mosleh Hmoud Al-Adhaileh
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Deanship of E-learning and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Mohammed Al-Yaari
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Chemical Engineering, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Zafar Iqbal Khan
- Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
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3
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Zhang J, Guo J, Lu D, Cao Y. ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis. Sci Rep 2024; 14:13696. [PMID: 38871844 DOI: 10.1038/s41598-024-64299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/06/2024] [Indexed: 06/15/2024] Open
Abstract
The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.
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Affiliation(s)
- Jian Zhang
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China.
| | - Jifeng Guo
- College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, 540004, China
| | - Donglei Lu
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
| | - Yuanyuan Cao
- School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
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4
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Kanwal A, Javed K, Ali S, Rubab S, Khan MA, Alasiry A, Marzougui M, Shabaz M. A hybrid framework for detection of autism using ConvNeXt-T and embedding clusters. THE JOURNAL OF SUPERCOMPUTING 2024; 80:8156-8178. [DOI: 10.1007/s11227-023-05761-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 08/25/2024]
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5
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Alharthi AG, Alzahrani SM. Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers. Brain Sci 2023; 13:1578. [PMID: 38002538 PMCID: PMC10670036 DOI: 10.3390/brainsci13111578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Researchers have explored various potential indicators of ASD, including changes in brain structure and activity, genetics, and immune system abnormalities, but no definitive indicator has been found yet. Therefore, this study aims to investigate ASD indicators using two types of magnetic resonance images (MRI), structural (sMRI) and functional (fMRI), and to address the issue of limited data availability. Transfer learning is a valuable technique when working with limited data, as it utilizes knowledge gained from a pre-trained model in a domain with abundant data. This study proposed the use of four vision transformers namely ConvNeXT, MobileNet, Swin, and ViT using sMRI modalities. The study also investigated the use of a 3D-CNN model with sMRI and fMRI modalities. Our experiments involved different methods of generating data and extracting slices from raw 3D sMRI and 4D fMRI scans along the axial, coronal, and sagittal brain planes. To evaluate our methods, we utilized a standard neuroimaging dataset called NYU from the ABIDE repository to classify ASD subjects from typical control subjects. The performance of our models was evaluated against several baselines including studies that implemented VGG and ResNet transfer learning models. Our experimental results validate the effectiveness of the proposed multi-slice generation with the 3D-CNN and transfer learning methods as they achieved state-of-the-art results. In particular, results from 50-middle slices from the fMRI and 3D-CNN showed a profound promise in ASD classifiability as it obtained a maximum accuracy of 0.8710 and F1-score of 0.8261 when using the mean of 4D images across the axial, coronal, and sagittal. Additionally, the use of the whole slices in fMRI except the beginnings and the ends of brain views helped to reduce irrelevant information and showed good performance of 0.8387 accuracy and 0.7727 F1-score. Lastly, the transfer learning with the ConvNeXt model achieved results higher than other transformers when using 50-middle slices sMRI along the axial, coronal, and sagittal planes.
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Affiliation(s)
| | - Salha M. Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia;
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6
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Awaji B, Senan EM, Olayah F, Alshari EA, Alsulami M, Abosaq HA, Alqahtani J, Janrao P. Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features. Diagnostics (Basel) 2023; 13:2948. [PMID: 37761315 PMCID: PMC10527645 DOI: 10.3390/diagnostics13182948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/07/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.
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Affiliation(s)
- Bakri Awaji
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 6646, Saudi Arabia; (M.A.); (H.A.A.); (J.A.)
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, Yemen
| | - Fekry Olayah
- Department of Information System, College of Computer Science and Information Systems, Najran University, Najran 6646, Saudi Arabia;
| | - Eman A. Alshari
- Department of Computer Science and Information Technology, Thamar University, Dhamar 87246, Yemen;
- Department of Artificial Intelligence, Faculty of Engineering and Smart Computing, Modern Specialized University, Sana’a, Yemen
| | - Mohammad Alsulami
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 6646, Saudi Arabia; (M.A.); (H.A.A.); (J.A.)
| | - Hamad Ali Abosaq
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 6646, Saudi Arabia; (M.A.); (H.A.A.); (J.A.)
| | - Jarallah Alqahtani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 6646, Saudi Arabia; (M.A.); (H.A.A.); (J.A.)
| | - Prachi Janrao
- Thakur College of Engineering and Technology, Kandivali(E), Mumbai 400101, India;
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7
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Li Y, Huang WC, Song PH. A face image classification method of autistic children based on the two-phase transfer learning. Front Psychol 2023; 14:1226470. [PMID: 37720633 PMCID: PMC10501480 DOI: 10.3389/fpsyg.2023.1226470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 07/17/2023] [Indexed: 09/19/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children's normal life. Screening potential autistic children before professional diagnose is helpful to early detection and early intervention. Autistic children have some different facial features from non-autistic children, so the potential autistic children can be screened by taking children's facial images and analyzing them with a mobile phone. The area under curve (AUC) is a more robust metrics than accuracy in evaluating the performance of a model used to carry out the two-category classification, and the AUC of the deep learning model suitable for the mobile terminal in the existing research can be further improved. Moreover, the size of an input image is large, which is not fit for a mobile phone. A deep transfer learning method is proposed in this research, which can use images with smaller size and improve the AUC of existing studies. The proposed transfer method uses the two-phase transfer learning mode and the multi-classifier integration mode. For MobileNetV2 and MobileNetV3-Large that are suitable for a mobile phone, the two-phase transfer learning mode is used to improve their classification performance, and then the multi-classifier integration mode is used to integrate them to further improve the classification performance. A multi-classifier integrating calculation method is also proposed to calculate the final classification results according to the classifying results of the participating models. The experimental results show that compared with the one-phase transfer learning, the two-phase transfer learning can significantly improve the classification performance of MobileNetV2 and MobileNetV3-Large, and the classification performance of the integrated classifier is better than that of any participating classifiers. The accuracy of the integrated classifier in this research is 90.5%, and the AUC is 96.32%, which is 3.51% greater than the AUC (92.81%) of the previous studies.
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Affiliation(s)
- Ying Li
- Guangxi Key Laboratory of Human-machine Interaction and Intelligent Decision, School of Logistics Management and Engineering, Nanning Normal University, Nanning, China
| | - Wen-Cong Huang
- Department of Sports and Health, Guangxi College for Preschool Education, Nanning, China
| | - Pei-Hua Song
- Guangxi Key Laboratory of Human-machine Interaction and Intelligent Decision, School of Logistics Management and Engineering, Nanning Normal University, Nanning, China
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8
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El Mouatasim A, Ikermane M. Control learning rate for autism facial detection via deep transfer learning. SIGNAL, IMAGE AND VIDEO PROCESSING 2023:1-8. [PMID: 37362233 PMCID: PMC10166045 DOI: 10.1007/s11760-023-02598-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/18/2023] [Accepted: 04/11/2023] [Indexed: 06/28/2023]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interaction and communication. Early detection of ASD can significantly improve outcomes for individuals with the disorder, and there has been increasing interest in using machine learning techniques to aid in the diagnosis of ASD. One promising approach is the use of deep learning techniques, particularly convolutional neural networks (CNNs), to classify facial images as indicative of ASD or not. However, choosing a learning rate for optimizing the performance of these deep CNNs can be tedious and may not always result in optimal convergence. In this paper, we propose a novel approach called the control subgradient algorithm (CSA) for tackling ASD diagnosis based on facial images using deep CNNs. CSA is a variation of the subgradient method in which the learning rate is updated by a control step in each iteration of each epoch. We apply CSA to the popular DensNet-121 CNN model and evaluate its performance on a publicly available facial ASD dataset. Our results show that CSA is faster than the baseline method and improves the classification accuracy and loss compared to the baseline. We also demonstrate the effectiveness of using CSA with L1-regularization to further improve the performance of our deep CNN model.
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Affiliation(s)
- Abdelkrim El Mouatasim
- Applied Mathematics and Computation Science (AMCS) Group, Faculty of Polydisciplinary Ouarzazate (FPO), Ibnou Zohr University, 45800 Ouarzazate, Morocco
| | - Mohamed Ikermane
- Applied Mathematics and Computation Science (AMCS) Group, Faculty of Polydisciplinary Ouarzazate (FPO), Ibnou Zohr University, 45800 Ouarzazate, Morocco
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9
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Alam MS, Rashid MM, Roy R, Faizabadi AR, Gupta KD, Ahsan MM. Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach. Bioengineering (Basel) 2022; 9:710. [PMID: 36421111 PMCID: PMC9687350 DOI: 10.3390/bioengineering9110710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/09/2022] [Accepted: 11/14/2022] [Indexed: 09/29/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child's behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.
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Affiliation(s)
- Md Shafiul Alam
- Department of Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia
| | - Muhammad Mahbubur Rashid
- Department of Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia
| | - Rupal Roy
- Department of Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia
| | - Ahmed Rimaz Faizabadi
- Department of Mechatronics Engineering, International Islamic University Malaysia, Kula Lumpur 43200, Malaysia
| | - Kishor Datta Gupta
- Computer and Information Science, Clark Atlanta University, Atlanta, GA 30314, USA
| | - Md Manjurul Ahsan
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA
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10
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Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder. ALGORITHMS 2022. [DOI: 10.3390/a15050166] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. Using various artificial intelligence (AI) techniques, ASD can be detected an at earlier stage than with traditional methods. The aim of this study was to propose a machine learning model that investigates ASD data of different age levels and to identify ASD more accurately. In this work, we gathered ASD datasets of toddlers, children, adolescents, and adults and used several feature selection techniques. Then, different classifiers were applied into these datasets, and we assessed their performance with evaluation metrics including predictive accuracy, kappa statistics, the f1-measure, and AUROC. In addition, we analyzed the performance of individual classifiers using a non-parametric statistical significant test. For the toddler, child, adolescent, and adult datasets, we found that Support Vector Machine (SVM) performed better than other classifiers where we gained 97.82% accuracy for the RIPPER-based toddler subset; 99.61% accuracy for the Correlation-based feature selection (CFS) and Boruta CFS intersect (BIC) method-based child subset; 95.87% accuracy for the Boruta-based adolescent subset; and 96.82% accuracy for the CFS-based adult subset. Then, we applied the Shapley Additive Explanations (SHAP) method into different feature subsets, which gained the highest accuracy and ranked their features based on the analysis.
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Mujeeb Rahman KK, Subashini MM. Identification of Autism in Children Using Static Facial Features and Deep Neural Networks. Brain Sci 2022; 12:brainsci12010094. [PMID: 35053837 PMCID: PMC8773918 DOI: 10.3390/brainsci12010094] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/03/2022] [Accepted: 01/08/2022] [Indexed: 01/27/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.
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Affiliation(s)
- K. K. Mujeeb Rahman
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India;
- Department of Biomedical Engineering, Ajman University, Ajman P.O. Box 346, United Arab Emirates
| | - M. Monica Subashini
- School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India
- Correspondence:
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12
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Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application. Brain Sci 2021; 11:brainsci11111446. [PMID: 34827443 PMCID: PMC8615807 DOI: 10.3390/brainsci11111446] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 11/22/2022] Open
Abstract
Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. Early intervention for children with ASD can help to improve their intellectual ability and reduces autistic symptoms. Multiple clinical researches have suggested that facial phenotypic differences exist between ASD children and typically developing (TD) children. In this research, we propose a practical ASD screening solution using facial images through applying VGG16 transfer learning-based deep learning to a unique ASD dataset of clinically diagnosed children that we collected. Our model produced a 95% classification accuracy and 0.95 F1-score. The only other reported study using facial images to detect ASD was based on the Kaggle ASD Facial Image Dataset, which is an internet search-produced, low-quality, and low-fidelity dataset. Our results support the clinical findings of facial feature differences between children with ASD and TD children. The high F1-score achieved indicates that it is viable to use deep learning models to screen children with ASD. We concluded that the racial and ethnic-related factors in deep-learning based ASD screening with facial images are critical to solution viability and accuracy.
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Aguglia E, Fusar-Poli L. Still a Long Way to Go. Editorial for the Special Issue "Understanding Autism Spectrum Disorder". Brain Sci 2021; 11:1062. [PMID: 34439681 PMCID: PMC8392122 DOI: 10.3390/brainsci11081062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/09/2021] [Accepted: 08/10/2021] [Indexed: 11/25/2022] Open
Abstract
Although many years have passed since the first descriptions of autism spectrum disorder (ASD) [...].
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Affiliation(s)
| | - Laura Fusar-Poli
- Department of Clinical and Experimental Medicine, Psychiatry Unit, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
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14
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Towards Autism Subtype Detection Through Identification of Discriminatory Factors Using Machine Learning. Brain Inform 2021. [DOI: 10.1007/978-3-030-86993-9_36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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