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

Authors: Fawaz Waselallah Alsaade; Mohammed Saeed Alzahrani
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

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%).


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## Body

## 1. Introduction

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]. These 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]. The earliest symptoms of ASD often appear within the first year of life [3–6] and may include lack of eye contact, lack of response to name calling, and indifference to caregivers. A small number of children appear to develop normally in the first year, and then show signs of autism between 18 and 24 months of age [5], including limited and repetitive patterns of behavior, a narrow range of interests and activities, and weak language skills. As these disorders also affect how a person perceives and socializes with others, children may suddenly become introverted or aggressive in the first five years of life as they experience difficulties in interacting and communicating with society. While ASD appears in childhood, it tends to persist into adolescence and adulthood [7].Advanced information technology that uses artificial intelligence (AI) models has helped to diagnose ASD early through facial pattern recognition. Yolcu et al. [8] used the convolutional neural network (CNN) algorithm to train data for extracting components of human facial expressions and proposed the use of such algorithm to detect facial expressions in many neurological disorders [9]. In 2018, Haque and Valles [10], using deep learning approaches, updated the Facial Expression Recognition 2013 dataset to recognize facial expressions of autistic children. Rudovic et al. [11] presented the CultureNet deep learning model that was used to identify 30 videos.Several studies have discovered important features of autism in numerous ways to detect autism, such as feature extraction [12], eye tracking [13], facial recognition, medical image analysis [9], and voice recognition [14]. However, facial recognition plays a more significant role in detecting autism than the person’s emotional state. Facial recognition is a common way to identify persons and to prove that they are normal or abnormal. It involves mining pertinent information to disclose behavior patterns [15, 16]. Duda et al. [17] introduced a new method of producing samples of differentials between autism and attention deficit hyperactivity disorder (ADHD) and using such differentials to recognize autism. Sixty five samples of differentials on facial expressions of social responsiveness were collected for the datasets. Deshpande et al. [18] designed metrics for studying brain activities to identify autism. AI and soft computing approaches have also been applied to detect autism. Different studies have been conducted on detecting autism, but few of them have focused on brain neuroimaging. Parikh et al. [19] used machine learning approaches to develop a system for extracting the characteristics of autism. They gathered their dataset from 851 persons whom they classified as having and not having ASD. Thabtah and Peebles [20] used rule-based machine learning (RBML) to detect ASD traits. Al Banna et al. [21] developed a smart system for monitoring ASD patients during the coronavirus disease 2019 (COVID-19) pandemic.Many real-life applications of AI and machine algorithms have been designed to help solve social problems. AI has been used in all health applications to help doctors control diseases such as autism. Artificial neural networks have been particularly focused on as a way to extract features of ASD patients that can be used to discriminate between persons with and without ASD. Among the techniques that have been used or proposed to detect autism in children are deep learning techniques, particularly, CNNs and recurrent neural networks (RNNs) [22, 23], and the bidirectional long short-term memory (BLSTM) model [24]. Lately, more studies have been conducted to diagnose ASD using machine learning approaches such as [24, 25], brain imaging [26–28], analysis of data on physical biomarkers [29–33], assessment of the behavior of persons with autism, and assessment of clinical data using the machine learning approach [33].Our study demonstrated the use of a well-trained classification model (based on transfer learning) to detect autism from an image of a child. With the advent of high-specification mobile devices, this model can readily provide a diagnostic test of putative autistic traits by taking an image with cameras. The main contributions of our research are as follows:(i)
Three pretrained deep learning algorithms were applied for ASD detection: NASNETMobile, Xception, and VGG19(ii)
The Xception model showed the best performance of the three pretrained deep learning algorithms(iii)
A system was designed to help health officials to detect ASD through eye and face identification(iv)
The developing system has been validated and examined using various methods.The rest of this paper is organized as follows. Section2 describes materials and methods of pretrained deep learning models. Section 3 provides experiments. Section 4 and 5 provide results and discussion. The conclusion of papers is presented in Section 6.


## 2. Materials and Methods

This study proposes a deep learning model based on transfer learning, namely, Xception, NASNETMobile, and VGG1 9 to detect autism using facial features of autistic and normal children. Facial features can be used to determine if a child has autism or is normal. The models extracted significant facial features from the images. One of the advantages offered by deep learning algorithms is the ability to extract very small details from an image, which a person cannot notice with the naked eye. Figure1 shows the framework of our study, from the data acquisition to the data preprocessing and loading, to the model preparation and training, and to the model performance test.Figure 1
The framework of our study.


### 2.1. Dataset

This study analyzed facial images of autistic children and normal children obtained from Kaggle platform, which is publicly accessible online [34]. The dataset consisted of 2,940 face images, half of which were of autistic children and the other half were of nonautistic children. This dataset was collected through Internet sources such as websites and Facebook pages that are interested in autism. Table 1 shows the distribution of the split dataset samples. The splitting of the input data is presented in Figure 2.Table 1
Splitting of the dataset for training, testing, and validation.
Total face imagesTraining set (%)Validation set (%)Testing set (%)2,9402,540100300Figure 2
Splitting of the dataset.


### 2.2. Preprocessing

The purpose of the data preprocessing was to clean and crop the images. Because the data were collected from Internet resources by Piosenka [34], they had to be preprocessed before they could be used to train the deep learning model. The dataset creator automatically cropped the face from the original image. Then, the dataset was split into 2,540 images for training, 100 for validation, and 300 for testing. To scaling, the normalization method was applied; the dataset was rescaling the parameters of all the images from the pixel values [0, 255] to [0, 1].


### 2.3. Convolutional Neural Network Models

AI has been remarkably developed to assist humans in their daily life, for example, through medical applications, which are based on a branch of AI called “computer vision.” Hence, the CNN algorithm has contributed to the detection of diseases and to behavioral and psychological analysis.


#### 2.3.1. Basic Components of the CNN Model

The convolutional neural network (CNN) is one of the most famous deep learning algorithms. It takes the input image and assigns importance to learnable weights and biases in order to recognize the class of the image. The neuron can be said to be a simulation of the communication pattern of the neurons of the human brain through the interconnection and communication between cells. In this section, we will explain the basic components of the CNN model: the input layer, convolutional layer, activating function, pooling layer, fully connected layer, and output prediction.


#### 2.3.2. Convolutional Layer with a Pooling Layer

The input of the convolutional layer is an image as a matrix of pixel values. The objective of the convolutional layer is to reduce the images into a form that can be easily processed without losing their important features that will help to detect autism. The first layer of the CNN model is responsible for extracting low-level features such as edges and color. The build of the CNN model allowed us to add more layers to it to enable it to extract the high-level features that will help it understand images. Due to the large number of parameters outputted from the convolution layer, which may significantly prolong the arithmetic operations of the matrices, the number of weights was reduced by using one of the following two techniques: max pooling or average pooling. Max pooling is based on the maximum values in each window in the stride, while average pooling is based on the mean value of each window in the stride. In this study, the model was based on max pooling. Figure3 shows the convolutional layer and the max pooling and average pooling processes. The slide window of the kernel extracts the features from the input image and converts the image into a matrix, after which the number of the parameters is mathematically reduced through max pooling and average pooling.Figure 3
The convolution layer and max pooling and average pooling.


#### 2.3.3. Fully Connected Layer and Activation Function

The fully connected (FC) layer is a nonlinear combination of the high-level features that received the input from the hidden layers and are represented as outputs. In the FC layer, the input image is represented as a column vector. The training of the model has two paths: the forward neural network and backpropagation. The forward neural network feeds form flattened output layer. In the backpropagation, the neural network minimizes the loss errors and learns more features by applying the number of the training iterations. Most deep learning models show high performance while increasing the number of the hidden layers and the training iterations, which allows the neural network to extract the low-level features deeply. The softmax classifier receives the parameters from the FC layer and calculates the properties to predict the output, as shown in Figure4. A Softmax output of 0 means the image belongs to class 0 and a Softmax output of 1 means the image belongs to class 1 In this study, class 0 is the autism class and class 1 is the normal class.Figure 4
The fully connected layer and the activation function.


### 2.4. Deep Learning Models

This paper is based on three pretrained models for autism detection using facial feature images: NASNetMobile, VGG19, and Xception.


#### 2.4.1. Xception Model

The 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. The inception layers are built from depthwise convolution layers and are followed by a pointwise convolution layer. Transfer learning has two concepts: feature extraction and fine-tuning. In this study, the feature extraction method used the pretraining model, which was trained on the standard dataset to extract the feature from the new dataset and to remove the top layers of the model. The new top layers were added to the model for custom classification based on the number of classes. Fine-tuning has been used to adapt the generic features to a given class to avoid overwriting. Figure 5 shows the network used in the Xception model architecture for extracting the image features for the dataset. The Xception architecture used the features maps, followed by a global max pooling layer and two dense layers, 128 and 64, respectively, with a rule activation function. Then, the output of the dense layers was passed to the flatten layer, which took the input as a feature map and the output as a vector. Batch normalization was used to enhance the output by avoiding overﬁtting. Keras supported the early-stopping method, which stopped the training when the validation loss of the model did not improve. In this model, the RMSprop optimizer was used to reduce the error learning rate or loss during the training of the parameters of the CNN model. The last layer for the output prediction used the Softmax function.Figure 5
Xception model architecture in our study.


#### 2.4.2. Visual Geometry Group Network (VGG) Model

VGG19 stands for the visual geometry group network (VGG19), a deep artificial neural network model with a 19 artificial multilayers process. Primarily, VGG19 is based on the CNN technique, is commonly implemented on the ImageNet dataset, and is valuable because of its straightforwardness, as 3 × 3 convolutional layers are attached to its upper side to upsurge with the gravity level. To reduce the input volume size, max pooling layers were employed as assigners in VGG19. In the VGG19 structure, two FC layers were adopted with 4,096 neurons to associate each layer with others in the model. The structure of VGG19 is presented in Figure6.(i)
Input layer: the function of this layer is to take and accept an image input with a size of 224 × 224 × 3. For the ImageNet race, the inventors of the model picked out the center 224 × 224 × 3 patch in each image to retain the input size of the image dependable.(ii)
Convolutional layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the minimum probable size that still releases up/down and left/right. The convolution stride is fixed at 1 pixel to conserve the spatial resolution after the convolution (the stride is the number of pixel shifts over the input matrix).(iii)
Hidden layers: all the hidden layers in the VGG network use ReLU. VGG does not frequently influence Local Response Normalization (LRN), as it increases the memory utilization and the training time. Furthermore, it does not enhance the overall accuracy of the model.(iv)
Fully connected layers: of the three FC layers, the first two had 4,096 channels each, and the third had 1,000 channels.Figure 6
VGG19 model.


## 2.1. Dataset

This study analyzed facial images of autistic children and normal children obtained from Kaggle platform, which is publicly accessible online [34]. The dataset consisted of 2,940 face images, half of which were of autistic children and the other half were of nonautistic children. This dataset was collected through Internet sources such as websites and Facebook pages that are interested in autism. Table 1 shows the distribution of the split dataset samples. The splitting of the input data is presented in Figure 2.Table 1
Splitting of the dataset for training, testing, and validation.
Total face imagesTraining set (%)Validation set (%)Testing set (%)2,9402,540100300Figure 2
Splitting of the dataset.


## 2.2. Preprocessing

The purpose of the data preprocessing was to clean and crop the images. Because the data were collected from Internet resources by Piosenka [34], they had to be preprocessed before they could be used to train the deep learning model. The dataset creator automatically cropped the face from the original image. Then, the dataset was split into 2,540 images for training, 100 for validation, and 300 for testing. To scaling, the normalization method was applied; the dataset was rescaling the parameters of all the images from the pixel values [0, 255] to [0, 1].


## 2.3. Convolutional Neural Network Models

AI has been remarkably developed to assist humans in their daily life, for example, through medical applications, which are based on a branch of AI called “computer vision.” Hence, the CNN algorithm has contributed to the detection of diseases and to behavioral and psychological analysis.


### 2.3.1. Basic Components of the CNN Model

The convolutional neural network (CNN) is one of the most famous deep learning algorithms. It takes the input image and assigns importance to learnable weights and biases in order to recognize the class of the image. The neuron can be said to be a simulation of the communication pattern of the neurons of the human brain through the interconnection and communication between cells. In this section, we will explain the basic components of the CNN model: the input layer, convolutional layer, activating function, pooling layer, fully connected layer, and output prediction.


### 2.3.2. Convolutional Layer with a Pooling Layer

The input of the convolutional layer is an image as a matrix of pixel values. The objective of the convolutional layer is to reduce the images into a form that can be easily processed without losing their important features that will help to detect autism. The first layer of the CNN model is responsible for extracting low-level features such as edges and color. The build of the CNN model allowed us to add more layers to it to enable it to extract the high-level features that will help it understand images. Due to the large number of parameters outputted from the convolution layer, which may significantly prolong the arithmetic operations of the matrices, the number of weights was reduced by using one of the following two techniques: max pooling or average pooling. Max pooling is based on the maximum values in each window in the stride, while average pooling is based on the mean value of each window in the stride. In this study, the model was based on max pooling. Figure3 shows the convolutional layer and the max pooling and average pooling processes. The slide window of the kernel extracts the features from the input image and converts the image into a matrix, after which the number of the parameters is mathematically reduced through max pooling and average pooling.Figure 3
The convolution layer and max pooling and average pooling.


### 2.3.3. Fully Connected Layer and Activation Function

The fully connected (FC) layer is a nonlinear combination of the high-level features that received the input from the hidden layers and are represented as outputs. In the FC layer, the input image is represented as a column vector. The training of the model has two paths: the forward neural network and backpropagation. The forward neural network feeds form flattened output layer. In the backpropagation, the neural network minimizes the loss errors and learns more features by applying the number of the training iterations. Most deep learning models show high performance while increasing the number of the hidden layers and the training iterations, which allows the neural network to extract the low-level features deeply. The softmax classifier receives the parameters from the FC layer and calculates the properties to predict the output, as shown in Figure4. A Softmax output of 0 means the image belongs to class 0 and a Softmax output of 1 means the image belongs to class 1 In this study, class 0 is the autism class and class 1 is the normal class.Figure 4
The fully connected layer and the activation function.


## 2.3.1. Basic Components of the CNN Model

The convolutional neural network (CNN) is one of the most famous deep learning algorithms. It takes the input image and assigns importance to learnable weights and biases in order to recognize the class of the image. The neuron can be said to be a simulation of the communication pattern of the neurons of the human brain through the interconnection and communication between cells. In this section, we will explain the basic components of the CNN model: the input layer, convolutional layer, activating function, pooling layer, fully connected layer, and output prediction.


## 2.3.2. Convolutional Layer with a Pooling Layer

The input of the convolutional layer is an image as a matrix of pixel values. The objective of the convolutional layer is to reduce the images into a form that can be easily processed without losing their important features that will help to detect autism. The first layer of the CNN model is responsible for extracting low-level features such as edges and color. The build of the CNN model allowed us to add more layers to it to enable it to extract the high-level features that will help it understand images. Due to the large number of parameters outputted from the convolution layer, which may significantly prolong the arithmetic operations of the matrices, the number of weights was reduced by using one of the following two techniques: max pooling or average pooling. Max pooling is based on the maximum values in each window in the stride, while average pooling is based on the mean value of each window in the stride. In this study, the model was based on max pooling. Figure3 shows the convolutional layer and the max pooling and average pooling processes. The slide window of the kernel extracts the features from the input image and converts the image into a matrix, after which the number of the parameters is mathematically reduced through max pooling and average pooling.Figure 3
The convolution layer and max pooling and average pooling.


## 2.3.3. Fully Connected Layer and Activation Function

The fully connected (FC) layer is a nonlinear combination of the high-level features that received the input from the hidden layers and are represented as outputs. In the FC layer, the input image is represented as a column vector. The training of the model has two paths: the forward neural network and backpropagation. The forward neural network feeds form flattened output layer. In the backpropagation, the neural network minimizes the loss errors and learns more features by applying the number of the training iterations. Most deep learning models show high performance while increasing the number of the hidden layers and the training iterations, which allows the neural network to extract the low-level features deeply. The softmax classifier receives the parameters from the FC layer and calculates the properties to predict the output, as shown in Figure4. A Softmax output of 0 means the image belongs to class 0 and a Softmax output of 1 means the image belongs to class 1 In this study, class 0 is the autism class and class 1 is the normal class.Figure 4
The fully connected layer and the activation function.


## 2.4. Deep Learning Models

This paper is based on three pretrained models for autism detection using facial feature images: NASNetMobile, VGG19, and Xception.


### 2.4.1. Xception Model

The 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. The inception layers are built from depthwise convolution layers and are followed by a pointwise convolution layer. Transfer learning has two concepts: feature extraction and fine-tuning. In this study, the feature extraction method used the pretraining model, which was trained on the standard dataset to extract the feature from the new dataset and to remove the top layers of the model. The new top layers were added to the model for custom classification based on the number of classes. Fine-tuning has been used to adapt the generic features to a given class to avoid overwriting. Figure 5 shows the network used in the Xception model architecture for extracting the image features for the dataset. The Xception architecture used the features maps, followed by a global max pooling layer and two dense layers, 128 and 64, respectively, with a rule activation function. Then, the output of the dense layers was passed to the flatten layer, which took the input as a feature map and the output as a vector. Batch normalization was used to enhance the output by avoiding overﬁtting. Keras supported the early-stopping method, which stopped the training when the validation loss of the model did not improve. In this model, the RMSprop optimizer was used to reduce the error learning rate or loss during the training of the parameters of the CNN model. The last layer for the output prediction used the Softmax function.Figure 5
Xception model architecture in our study.


### 2.4.2. Visual Geometry Group Network (VGG) Model

VGG19 stands for the visual geometry group network (VGG19), a deep artificial neural network model with a 19 artificial multilayers process. Primarily, VGG19 is based on the CNN technique, is commonly implemented on the ImageNet dataset, and is valuable because of its straightforwardness, as 3 × 3 convolutional layers are attached to its upper side to upsurge with the gravity level. To reduce the input volume size, max pooling layers were employed as assigners in VGG19. In the VGG19 structure, two FC layers were adopted with 4,096 neurons to associate each layer with others in the model. The structure of VGG19 is presented in Figure6.(i)
Input layer: the function of this layer is to take and accept an image input with a size of 224 × 224 × 3. For the ImageNet race, the inventors of the model picked out the center 224 × 224 × 3 patch in each image to retain the input size of the image dependable.(ii)
Convolutional layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the minimum probable size that still releases up/down and left/right. The convolution stride is fixed at 1 pixel to conserve the spatial resolution after the convolution (the stride is the number of pixel shifts over the input matrix).(iii)
Hidden layers: all the hidden layers in the VGG network use ReLU. VGG does not frequently influence Local Response Normalization (LRN), as it increases the memory utilization and the training time. Furthermore, it does not enhance the overall accuracy of the model.(iv)
Fully connected layers: of the three FC layers, the first two had 4,096 channels each, and the third had 1,000 channels.Figure 6
VGG19 model.


## 2.4.1. Xception Model

The 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. The inception layers are built from depthwise convolution layers and are followed by a pointwise convolution layer. Transfer learning has two concepts: feature extraction and fine-tuning. In this study, the feature extraction method used the pretraining model, which was trained on the standard dataset to extract the feature from the new dataset and to remove the top layers of the model. The new top layers were added to the model for custom classification based on the number of classes. Fine-tuning has been used to adapt the generic features to a given class to avoid overwriting. Figure 5 shows the network used in the Xception model architecture for extracting the image features for the dataset. The Xception architecture used the features maps, followed by a global max pooling layer and two dense layers, 128 and 64, respectively, with a rule activation function. Then, the output of the dense layers was passed to the flatten layer, which took the input as a feature map and the output as a vector. Batch normalization was used to enhance the output by avoiding overﬁtting. Keras supported the early-stopping method, which stopped the training when the validation loss of the model did not improve. In this model, the RMSprop optimizer was used to reduce the error learning rate or loss during the training of the parameters of the CNN model. The last layer for the output prediction used the Softmax function.Figure 5
Xception model architecture in our study.


## 2.4.2. Visual Geometry Group Network (VGG) Model

VGG19 stands for the visual geometry group network (VGG19), a deep artificial neural network model with a 19 artificial multilayers process. Primarily, VGG19 is based on the CNN technique, is commonly implemented on the ImageNet dataset, and is valuable because of its straightforwardness, as 3 × 3 convolutional layers are attached to its upper side to upsurge with the gravity level. To reduce the input volume size, max pooling layers were employed as assigners in VGG19. In the VGG19 structure, two FC layers were adopted with 4,096 neurons to associate each layer with others in the model. The structure of VGG19 is presented in Figure6.(i)
Input layer: the function of this layer is to take and accept an image input with a size of 224 × 224 × 3. For the ImageNet race, the inventors of the model picked out the center 224 × 224 × 3 patch in each image to retain the input size of the image dependable.(ii)
Convolutional layers: VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the minimum probable size that still releases up/down and left/right. The convolution stride is fixed at 1 pixel to conserve the spatial resolution after the convolution (the stride is the number of pixel shifts over the input matrix).(iii)
Hidden layers: all the hidden layers in the VGG network use ReLU. VGG does not frequently influence Local Response Normalization (LRN), as it increases the memory utilization and the training time. Furthermore, it does not enhance the overall accuracy of the model.(iv)
Fully connected layers: of the three FC layers, the first two had 4,096 channels each, and the third had 1,000 channels.Figure 6
VGG19 model.


## 3. Experiments

The results of the deep learning models are presented in this section, and the significant results of developing system are declared.


### 3.1. Experimental Setup

The experiment was executed on different libraries of python and hardware devices for developing an intelligent autism detection system (ADS). Table2 shows the main requirements for the design of the ADS.Table 2
Experimental setup.
HardwareSoftware/librariesProcessor core I7TensorFlow libraryKeras libraryPanda seabornMatplotlibNumpy8 GB RAMNote. The model parameters were set with 100 epochs and with a batch size of 32. To avoid overfitting and to optimize the training time, the early stop strategy was used, whereby the training was stopped after 28 epochs. The parameters of the deep learning model are shown in Table 3.Table 3
Parameters used in the pretrained deep learning models.
Parameter nameValueGlobal max pooling layer size3∗ 3Dense layer128, 64Batch size32Number of epochs100Output classification layerSoftmaxOptimizerADAMActivation functionRule


### 3.2. Evaluation Metrics

This study uses different types of performance evaluation metrics for the three pretrained models such as accuracy, sensitivity, and speciﬁcity and a confusion matrix. A confusion matrix is a type of measure of classification performance that represents a table of the true and false values of the testing results. In the confusion matrix of the Xception model, the True Positives were 132 autistic children out of 150 autistic children, the False Negatives were 18 children classified as autistic children, the True Negatives were 141 correctly classified children out of the 150 normal children, and the False Positives were 9 children.The equations for these metrics are as follows:(1)Accuracy=TP+TNFP+FN+TP+TN×100%,Specificity=TNTN+FN×100%,Sensitivity=TPTP+FP×100%,where TP is the True Positive, FP is the False Positive, TN is the True Negative, and FN is the False Negative. Specificity is the capacity of the model to correctly identify the normal children, and sensitivity is the capacity of the model to correctly identify autistic children.


## 3.1. Experimental Setup

The experiment was executed on different libraries of python and hardware devices for developing an intelligent autism detection system (ADS). Table2 shows the main requirements for the design of the ADS.Table 2
Experimental setup.
HardwareSoftware/librariesProcessor core I7TensorFlow libraryKeras libraryPanda seabornMatplotlibNumpy8 GB RAMNote. The model parameters were set with 100 epochs and with a batch size of 32. To avoid overfitting and to optimize the training time, the early stop strategy was used, whereby the training was stopped after 28 epochs. The parameters of the deep learning model are shown in Table 3.Table 3
Parameters used in the pretrained deep learning models.
Parameter nameValueGlobal max pooling layer size3∗ 3Dense layer128, 64Batch size32Number of epochs100Output classification layerSoftmaxOptimizerADAMActivation functionRule


## 3.2. Evaluation Metrics

This study uses different types of performance evaluation metrics for the three pretrained models such as accuracy, sensitivity, and speciﬁcity and a confusion matrix. A confusion matrix is a type of measure of classification performance that represents a table of the true and false values of the testing results. In the confusion matrix of the Xception model, the True Positives were 132 autistic children out of 150 autistic children, the False Negatives were 18 children classified as autistic children, the True Negatives were 141 correctly classified children out of the 150 normal children, and the False Positives were 9 children.The equations for these metrics are as follows:(1)Accuracy=TP+TNFP+FN+TP+TN×100%,Specificity=TNTN+FN×100%,Sensitivity=TPTP+FP×100%,where TP is the True Positive, FP is the False Positive, TN is the True Negative, and FN is the False Negative. Specificity is the capacity of the model to correctly identify the normal children, and sensitivity is the capacity of the model to correctly identify autistic children.


## 4. Results

This section presents the testing results of the experiments conducted to detect ASD. Table4 summarizes the testing results of the used deep learning models.Table 4
Testing results of the pretrained deep learning models.
Model nameSpecificitySensitivityAccuracyXception0.940.880.91VGG190.830.780.80NASNETMobile0.750.820.78In these experiments, three different pretrained deep learning models, namely, Xception, VGG19, and NASNetMobile were implemented to detect ASD. Each model was trained and tested to extract the traits that categorize children as autistic and as normal based on their facial features. Figure7 shows the confusion metrics of the three deep learning models. They show that the Xception model had the highest testing accuracy, 91%, of the three models, and the NASNetMobile model had the lowest performance level of 78%. Although the dataset was collected from Internet sources by the data generator, which clearly showed the difference in ages and the quality of the photography, the Xception model showed the highest accuracy, with only a small percentage of errors.Figure 7
Confusion matrices for the. (a) VGG19 model, (b) NASNetmobile model, and (c) Xception model.
(a)(b)(c)The performance of the VGG19 model for the training and validation of the data for ASD detection is presented in Figure8(a), with the y-axis representing the score percentage and the x-axis indicating the number of epochs. In the training process, the accuracy of the VGG19 model rose from 56% to 85% after 25 epochs, and in the validation process, its accuracy was 82%. Its training and validation losses are presented in Figure 8(b). Its training loss was 3.5, and its validation loss was 2.5.Figure 8
Overall performance plot for the VGG19 model. (a) Model performance. (b) Model loss.
(a)(b)Figure9 shows the performance of the NASNetMobile model in detecting ASD. The graphic plot shows that the model did not have good results. Its training and validation accuracy percentages are shown in Figure 9(a), with the y-axis representing the score percentage and the x-axis indicating the number of epochs. Its accuracy in the training stage was 70–90% and in the validation stagewas 65–78%. It had good results in the training phase but poorer results in the validation phase than another deep learning model. Its entropy training and validation losses are presented in Figure 9(b). Its training loss was 4.0, and its validation loss was 3.2. Its performance plot shows overfitting in the training process, which explains why its accuracy percentage was lower.Figure 9
Overall performance plot for the NASNetMobile model. (a) Model performance. (b) Model loss.
(a)(b)The Xception model achieved 91% accuracy for the testing data and 100% accuracy for the training data. Figure10(a) shows its accuracy performance. Its accuracy in the training process was 100% and in the validation process was from 70% to 91%. It was observed that the Xception model is the appropriate deep learning model for detecting ASD. Its validation loss is shown in Figure 10(b) as 2.0.Figure 10
Plot of the training and validation accuracy of the Xception model. (a) Accuracy performance. (b) Model loss.
(a)(b)


## 5. Results and Discussion

People with autism face difficulties and challenges in understanding the world around them and in understanding their thoughts, feelings, and needs. The world surrounding a person with autism seems to him like a horror movie, and he finds some sounds and lights and even smells and tastes of foods frightening and sometimes painful. Thus, when a sudden change occurs in their world, they are terrified that no one else can understand.Diagnosing autism is very important to save the lives of many children. Developing an intelligence system based on AI can help identify autism early. In this study, three advanced deep learning models, namely, Xception, VGG19, and NASNetMobile were considered for use in diagnosing autism. The empirical results of these models were presented, and it was noted that the Xception model attained the highest accuracy of 91%.The results of the comparative prediction analysis of the Xception model and the existing system are presented in Table5. Musser [35] used the VGGFace model that attained 85% accuracy. Tamilarasi et al. [36, 37] used deep learning ResNet50 to detect autism but with small data, that is, only 49 images. Its accuracy was 89%. Jahanara et al. [38] introduced the VGG19 model to identify autism through facial images. The VGG19 model achieved 84% accuracy in the validation accuracy process.Table 5
Comparison of the results of our system and existing models.
AuthorDatasetsModelAccuracy (%)Han et al. [35]Same as our datasetVGGFace85.0Haque et al. [10]The dataset included 19 TD and 20ASD childrenResNet5089.2Beary et al. [38]Same as our datasetVGG1984.0Developing modelMobileNet91.0In this study, three deep learning algorithms were used to detect autism. We observed that the Xception model showed significant accuracy, 91%, compared with existing systems. Figure11 shows the results of the comparison of our developing system with various existing systems. Overall, our developing system outperformed all the existing systems.Figure 11
Comparison results of the developing system with existing systems.


## 6. Conclusions

Interest in child autism has risen due to the advances in global health know-how and capacities. Moreover, the number of autistic children has increased in recent years, due to which researchers and academics have intensified their efforts to uncover the causes of autism and to detect it early in order to give autistic people behavioral development treatment programs that should help them integrate into society and leave the isolation of the autistic world.This paper evaluated the performance of three deep learning models in detecting ASD through facial features: NASNETMobile, Xception, and VGG19. Each model was trained on a publicly available dataset on the Internet, and the best result for classification accuracy was achieved by the Xception model (91%). The results of the model classification showed us the possibility of using such models based on deep learning and computer vision as automatic tools for specialists and families to accurately and more quickly diagnose autism. Computer techniques contribute to the successful conduct of complex behavioral and psychological analyses for autism diagnosis that require a longer time and great effort.



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