A Hybrid Model to Guide the Consultation of Children with Autism Spectrum Disorder

Nunes, Luciano Comin; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calí­ope Dantas; Pompeu, Monica; Filho, Marum Simão; Comin-Nunes, Rafael; Pinheiro, Pedro Gabriel Calíope Dantas

doi:10.1007/978-3-030-30809-4_38

Cited by 8 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e Xception model was trained on the ImageNet dataset [2,3] for the image recognition and classification task. Xception is a deep CNN that provides new inception layers.…”

Section: Xception Modelmentioning

confidence: 99%

“…Autism spectrum disorders (ASD) refer to a group of complex neurodevelopmental disorders of the brain such as autism, childhood disintegrative disorders, and Asperger's syndrome, which, as the term "spectrum" implies, have a wide range of symptoms and levels of severity [1]. ese disorders are currently included in the International Statistical Classification of Diseases and Related Health Problems under Mental and Behavioral Disorders, in the category of Pervasive Developmental Disorders [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms

Alsaade

Al-zahrani

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum disorder (ASD) is a neurological disease correlated with brain growth that later impacts the physical impression of the face. Children with ASD have dissimilar facial landmarks, which set them noticeably apart from typically developed (TD) children. Novelty of the proposed research is to design a system that is based on autism spectrum disorder detection on social media and face recognition. To identify such landmarks, deep learning techniques may be used, but they require a precise technology for extracting and producing the proper patterns of the face features. This study assists communities and psychiatrists in experimentally detecting autism based on facial features, by using an uncomplicated web application based on a deep learning system, that is, a convolutional neural network with transfer learning and the flask framework. Xception, Visual Geometry Group Network (VGG19), and NASNETMobile are the pretrained models that were used for the classification task. The dataset that was used to test these models was collected from the Kaggle platform and consisted of 2,940 face images. Standard evaluation metrics such as accuracy, specificity, and sensitivity were used to evaluate the results of the three deep learning models. The Xception model achieved the highest accuracy result of 91%, followed by VGG19 (80%) and NASNETMobile (78%).

show abstract

“…e Xception model was trained on the ImageNet dataset [2,3] for the image recognition and classification task. Xception is a deep CNN that provides new inception layers.…”

Section: Xception Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms

Alsaade

Al-zahrani

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…It obtained 100% (one hundred percent) accuracy in detecting the disorder when considering the protocol described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) [ 8 ]. Also, in this same line, Nunes et al [ 3 ] present an intelligent solution based on a hybrid model of the expert system associated with the multicriteria method MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) to support the early diagnosis of ASD in children aged up to two years [ 3 ].…”

Section: Theoretical Referencementioning

confidence: 99%

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis

Andrade

Portela

Pinheiro

et al. 2021

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms’ composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

show abstract

“…These illnesses can vary greatly in terms of severity and symptomatology, as the term "spectrum" indicates [1]. The International Statistical Classification of Diseases (ISCD) and Related Health Problems (RHP) now include these diseases as Omnipresent Developmental diseases (PDD) under Mental and Behavioral Disorders (MBD) [2]. Early childhood symptoms of ASD often appear in the first few years of life (age of 3-6 years) and may include a lack of eye contact, apathy toward caregivers, and a failure to react when called names [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model For Autism Diagnosing

R. Swadi,

S. Croock

2024

IJCDS

View full text Add to dashboard Cite

The brain development and physical appearance of the face are both impacted by the neurologic disorder known as Autism Spectrum Disorder (ASD). Children with ASD exhibit different facial landmarks from children who normally grow with Typical Developing (TD), despite the fact that the disorder is thought to be inherited. When a child's behavioral traits and facial features are examined, the likelihood of an accurate diagnosis is highest. In recent years, deep convolutional neural networks widely used in image processing because they can effectively extract features from images and learn to recognize patterns, making them well-suited for tasks such as object detection, image segmentation, and classification. In this paper, we provide a deep learning CNN-based model for diagnosing autism that divides children into two categories depending on their facial features: possibly healthy and potentially unhealthy. The Tesorflow and Keras libraries are used by the suggested deep learning model to carry out feature extraction and picture classification. The dataset obtained from the Kaggle repository is used to train and evaluate the model. The dataset that was used to test this model consisted of 2,122 that were excluded from the original 2,940 images due to the quality and race. The testing of the proposed model results in an Area Under Curve (AUC) of 99.8% and an accuracy of 97.3%. This model proves its high diagnosis accuracy, ease of use, and fast decision.

show abstract

A Hybrid Model to Guide the Consultation of Children with Autism Spectrum Disorder

Cited by 8 publications

References 8 publications

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis

Deep Learning Model For Autism Diagnosing

Contact Info

Product

Resources

About