PRESENTATION-214718032

1. Two Goal Learning to Classify Autistic and Non-autistic Face
by Using Transfer Learning
SHAHZAD INZAMAM 214718032
Supervisor : Liu Jin
Master Degree
December 2023

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Presentation Outline
• Introduction
• Autism Spectrum Disorder
• Current Diagnosis Methods
• Transfer Learning for Autistic Disease Prediction
• Proposed Methodology
• Primary Result Experiment
• Conclusion

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Introduction
Autism Spectrum Disorder (ASD) affects millions of people worldwide,
and its diagnosis remains a challenge for healthcare professionals.
However, recent advancements in machine learning have shown promising
results in predicting ASD with high accuracy. Transfer learning, in
particular, has emerged as a powerful tool for improving the accuracy of
ASD diagnosis.
In this presentation, we will explore the potential of transfer learning in
predicting ASD and its importance in revolutionizing the field of
healthcare. We will discuss current diagnosis methods, their limitations, and
how transfer learning can be used to overcome these limitations and
discover how cutting-edge technology can improve the lives of individuals
with ASD.
Fig 1: ASD Samples Images
Kaggle ASD Dataset

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Autism Spectrum Disorder
• Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects communication, social interaction,
and behavior. It is a spectrum disorder, meaning that the severity of symptoms can vary widely from person to person.
Some individuals with ASD may have difficulty with verbal and nonverbal communication, while others may have
repetitive behaviors or intense interests in specific topics.
• According to the Centers for Disease Control and Prevention (CDC), approximately 1 in 54 children in the United
States are diagnosed with ASD. While the exact causes of ASD are still unknown, research suggests that a
combination of genetic and environmental factors may play a role. It is important to note that ASD is not caused by
bad parenting or vaccines, as some myths suggest.
• Real-life examples of individuals with ASD include Temple Grandin, The individuals demonstrate that having autism
does not limit one's potential or ability to make a positive impact on the world.

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Late detection of ASD in patients can be attributed to several key factors:
1. Diagnosis of ASD primarily relies on interactive sessions, necessitating the involvement of clinical experts who can
accurately diagnose children around the age of two [16].
2. Limited access to specialists poses a challenge for parents, particularly in rural communities or underdeveloped
countries, where the availability of such physicians is scarce [17].
3. Lack of familiarity and awareness about ASD among parents often leads them to overlook developmental issues in
their children and not consider them as symptoms of a potential disorder.
4. Furthermore, children from racial and ethnic minority backgrounds who undergo initial screening are less likely to
undergo subsequent medical examinations due to the high costs associated with the required sophisticated
equipment and skilled personnel [18].

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• Currently, the most common method for diagnosing Autism Spectrum Disorder (ASD) is through behavioral
assessments and interviews with parents or caregivers. These assessments can be time-consuming and rely heavily on
subjective observations, which can lead to misdiagnosis or delayed diagnosis. Additionally, these assessments may not
capture subtle differences in behavior that are indicative of ASD.
• Another method for diagnosing ASD is through brain imaging techniques such as magnetic resonance imaging (MRI)
and electroencephalography (EEG). While these methods can provide valuable insights into brain function, they are
expensive and require specialized equipment and expertise. Furthermore, they may not be accessible to all individuals,
particularly those in low-resource settings.
• Transfer learning has the potential to improve the accuracy [5] of ASD diagnosis by leveraging existing data and
models from related fields. By using pre-trained models [6] and adapting them to ASD prediction, transfer learning
can overcome the limitations of current diagnosis methods and provide a more objective and efficient approach to
diagnosis.
Current Diagnosis Methods

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In the realm of ASD patient diagnosis, several distinct technologies are available for the detection of autism. These
include:
1. Child Autism Rating Scale (CARS)
2. Autism Diagnostic Observation Schedule (ADOS)
3. Autism Detection in Early Childhood (ADEC)
4. Autism Diagnostic Interview – revised (ADI-R)
5. Social Communication Questionnaire (SCQ) Fig 2: Images of dataset containing the both autistic and non-
autistic faces
These technologies serve as invaluable resources in the diagnostic process, contributing to a more comprehensive
understanding of ASD and enabling healthcare professionals to make accurate assessments and tailored interventions for
individuals with autism.

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Transfer Learning for ASD Prediction
After conducting a comprehensive examination of the current research landscape both domestically and
internationally, it is evident that there is a pressing requirement for the development of a convolutional neural
network (CNN) base on transfer learning architecture capable of detecting ASD with minimal hyperparameters
and low computation cost due to pre-trained weights. This imperative endeavor aims to facilitate the creation of
an effective Transfer learning-based ASD diagnosis model. Presently, the research conducted in this domain is
still in its nascent stages, and numerous unresolved challenges persist.

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Main research ideas, and academic innovations
Our proposal introduces a highly robust face recognition framework
utilizing transfer learning, offering the potential for superior accuracy in
the identification of children with autism.
 In this research, we conduct an in-depth analysis of an optimized
ResNet101 and EfficientNetB5 model, surpassing hybrid deep
learning approaches by employing deep learning methods to
accurately detect ASD.
 ASD is characterized by specific facial traits in children, such as a
notably wide upper face with widely spaced eyes, and a relatively
short middle face section encompassing the cheeks and nose.
Consequently, our primary contribution lies in developing an expert
system that leverages facial landmarks to identify ASD in children,
thereby enhancing the healthcare system in China and enabling
early detection of ASD.
Academic Innovation
The research methodology includes the following steps:
 Dataset preprocessing
 Ablation study
 Optimal hyperparameter selection
 Build Hybrid-Net
 Analysis of model performance
Main Research Idea
Fig 3: Flow diagram of Convolution Neural Network Model

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Proposed Methodology
Table 1 ASD: Kaggle ASD Dataset (Access 2023)
Category Subcategory Dataset Division
Train Validate Test
Disorder Autistic 1263 100 100
No Autistic 1263 100 100
Table 2 Dataset Division
Kaggle ASD Dataset
Fig 4:Datasets Samples of Autistic Images
Category Subcategory Total Image
Disorder Autistic 1463
No Autistic 1463

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Fig 5:Proposed Methodology for Autistic Prediction
Proposed Methodology

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ResNet Basic Architecture
Fig 6: ResNet-Based Architecture

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EfficientNetB5 Basic Architecture
Fig 7: EfficientNetB5 Based Architecture

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Performance evaluation metrics
Primary Result Experiment

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Experiment Setup
The deep learning ResNet101 and EfficientNetB5 models hybridized were applied to detect ASDs. As a result,
Jupyter Notebook, based on Python, was responsible for all of the calculations that were altered for developing a
classification of ASDs in children. Several methods may be used to detect ASDs. Individual classification models
were trained using the 90% training set, and their efficacy was evaluated using the 10% test set. Both sets were
utilized in conjunction with the training set. For the purpose of determining how well these classifiers functioned,
a number of assessment measures, including visualize by Confusion Metrix accuracy, area under the curve
(AUC), sensitivity, specificity, precision, and F1-score, were computed.

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Primary Result
Metrics Evaluations
Accuracy Precision Recall F1-Score
Kaggle Autistic
Dataset
ResNet101 (Base) 86 87 86 86
EfficientNetB5 (Base) 83 84 83 83
Table 3 Model Accuracy, Precision, Recall, and F1-Score (Kaggle Autistic Dataset)

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Primary Result
Confusion Matrices
ResNet101
88
85
12
15
Autistic
Autistic
No
Autistic
No Autistic
EfficientNetB5
73
93
27
7
Autistic
Autistic
No
Autistic
No Autistic
Fig 8: ResNet101 & EfficientNetB5 Based Architecture Confusion Matrices

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Primary Result
Training and Validation Accuracy
Fig 9: RestNet101 & EfficientNetB5 Training Accuracy

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Primary Result
Training and Validation Loss
Fig 9: RestNet101 & EfficientNetB5 Training Loss

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Primary Result
Training and Validation Loss
Reference Model Dataset Accuracy %
Mujeeb Rahman et al [9] MobileNet Kaggle Dataset 87
Thabtah et al [32] Xception Kaggle Dataset 90
Alsaade et al [31] Xception Kaggle Dataset 91
Alkahtani et al [33] VGG16 + MobileNet Kaggle Dataset 92
Rabbi et al [34] CNN Model Kaggle Dataset 92.31
Arumugam et al [35] CNN Model Kaggle Dataset 91
Akter et al [36] MobileNet-V1+K-Mean
clustering
Kaggle Dataset 92.10
Table 4 Comparative Analysis with Existing Studies

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Conclusion
The early diagnosis of ASD in children has been shown to have significant positive effects on the diagnosed child’s
long-term health outcomes. All the detection methods used at present rely on the judgement of professionals, despite
the fact that this approach is both subjective and costly.
In this study, we recommended using a deep learning system that integrates several facial landmark features in order to
identify children who have ASD. The utilization of this system could reduce costs and increase the effectiveness of the
detection process. To start, we devised a unique approach for recognizing the properties of facial landmarks.
In this study, we made use of a dataset (Kaggle Autistic Dataset) that is available to the general public and includes
images of the faces of both autistic and typically developing children.
Pre-trained models for binary ASD classification were developed and assessed using ResNet101, and EfficientNetB5
models employing these also constructed. When compared to the other models we examined, we discovered that the
ResNet101 model had the best accuracy (86%). The results suggest 2926 19 of 21 that still images of children’s faces
might be used to rapidly gather diagnostic indicators of ASD, thus enabling an ASD screening approach that is both
rapid and accurate.

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26. 感谢各位老师聆听！
THANKS FOR ALL