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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5234_Библиотеки_им_академика_М_И_Перельмана
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114 Computational Intelligence Algorithms
Speech language therapy improves interaction and communication among other
skills.
Farooq et al. (2023) utilized support vector machine and logistic regression to
classify the ASD factors and detection of ASD. Four different datasets were analyzed for children, for which the proposed model achieves 98% accuracy, and for
adults, for which 81% accuracy as achieved [1].
Al-disbat et al. (2018) experimented with fuzzy data mining algorithms for ASD
kids for classication in the University of California, Irvine Machine Learning
(UCIML) repository dataset. The researchers evaluated various models of FUZZY
and found the FURIA model produced better results than others [2]. Thabath
et al. (2020) proposed rules in the machine leaning (ML) model for classication
and showed the actual cause for this disease. The researchers used this model in
experiments with children, adolescents, and adults datasets with boosting, bagging,
decision trees, and rule induction algorithms and produced higher accuracy [3].
Wingeld et al. (2020) developed an ML embedded mobile application to monitor
autism detection and identied that the random forest algorithm AUCROC produced
accuracy of 98%, and this application helps in many ways to identify ASD [4]. Leroy
et al. (2024) analyzed the ML model, three deep learning (DL) models, and various
ensemble models for ASD detection and found that majority voting with BiLSTM
Ml achieved 100% precision, 91% accuracy, 100% specicity, and a 0.91 F1 score
[5]. Reddy et al. (2023) used a facial image dataset for CNN, VGG16, VGG19, and
the EfcientNet BO classication and attained accuracy of 84.66%, 98.50%, and
87.9% for each algorithm [6].
Alsaade et al. (2022) developed a model with web application and DL algorithms
such as CNN, Xception, VGG19, and NASNETMobile to classify the facila autism
image dataset and attained accuracy of 78% for NASNETMobile, 80% accuracy for
VGG19, and 91% accuracy for the Xception model [7]. Khosla et al. (2021) proposed
a DL model to classify facial images of healthy and autistic children and removed
the duplicate images. The Mobile net model attained the accuracy of 87% in classication [8].
Research gaps include the following:
• Identication and treatment of ASD takes more time, which leads to proper
medical practice.
• Many researchers experiment with DL and ML for ASD analysis, yet they
struggle to attain high accuracy.
• Many researchers experiment with feature datasets instead of image
datasets.
9.6 METHODOLOGY
The failure of ML technique was the basic reason to develop a DL algorithm. DL is
an advanced technology of ML and articial intelligence (AI). It can handle large
sets of data and doesn’t need external feature extraction techniques. It contains multiple layers of neural networks and hidden layers, and every process is carried out
promptly. This architecture can process large data in accurate manner within short
115 Intelligent Deep Learning Algorithms
time. DL algorithms play a signicant role in the medical eld due to its efcient
and timely analysis [9]. Every algorithm performs its role in unique way due to its
interconnected neurons and ability to learn from the given data [10]. DL models have
an ability to capture dominant features and works in an end-to-end fashion. It plays
a dominant role in classication, object detection, segmentation, speech recognition,
sentiment analysis, medical analysis, predictive analysis, fraud detection, recommender systems, etc. [11, 12]. This research analyzed DL techniques such as CNN,
RNN-CNN, LSTM-CNN, VGG-16, and ResNet-50 for autism detection.
9.6.1 DATASETS
The facial dataset for ASD is picked from the Kaggle dataset, and it contains
1,468 autism les and 1,468 nonautism les. The training folder contains 1,628 for
autism and 1,628 for nonautism. At the same time, the test folder contains 150 for
autism and 150 for nonautism images. The validation folder contains 50 autism and
50 nonautism images. A total of 5,874 images were experimented with for this study.
Figure 9.2 shows the autism and nonautism images in the dataset. Image resizing is
carried out for image preprocessing.
Figure 9.3 shows the overall methodology of the ASD classication.
9.6.2 CNN
This network was developed to execute grid information and mainly used for image
analysis. Convolution layer, activation function, stride, padding, pooling layer, batch
normalization, attening, and fully connected layers are the basic operations and
building layers for CNN. Convolution layers works with input and lters by dot product and produce feature maps by capturing dominant features. Pooling layers are
utilized to reduce the dimensionality of feature maps and can be either max pooling
or average pooling. Figure 9.4 shows the layers and architecture of CNN [13].
FIGURE 9.2 Autism and nonautism images.
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FIGURE 9.3 Schematic model of workow.
The max pooling fetches the maximum value from the feature map by ltering,
and average pooling considers the average value from the feature map region. This
research utilized the concept of max pooling. The activation function helps to identify the nonlinearity in the model and learned complex features. Every layer used
the concept of the same padding to protect the edge features by adding zero. Fully
FIGURE 9.4 CNN layer architecture.
117 Intelligent Deep Learning Algorithms
Ht = f (H
t−1
, xt)
t1−
= tanh WH
t
)
H
t
(
hh t −1
+ Wx
xh
yt = W
hy
,H
t
y
t
connected layers work as same as dense layers in ML and fetch the nal feature maps
with dominant patterns. The activation function sigmoid produces binary output for
classication.
9.6.3 RNN
This network processes sequential and temporal data (see Figure 9.5). Hidden
layers in RNN capture data from a previous sequence with the help of hidden layers. It works similar to CNN, and it also has memory to capture previous data. The output depends on previous data. The important property of
RNN is the hidden state or memory state, which reduces the complexity of
parameters.
The recurrent unit is the basic unit, and it keeps the hidden state. The hidden state
holds the knowledge of previous time step and is updated as per the following formula. The current state can be calculated by
ent state,
is the previous state, and t is the initial setup. The activation function
performs as follows:
where hh is recurrent neuron weight and xh is the input neuron weight. The output layer is computed as
, where
is output and hy is the output layer
weight.
Back propagation helps to update these parameters. This process is repeated until
the calculation is made for output. The nal output is compared with actual output, and bias is propagated back to the network for changing the weight. Hence it
remembers every detail of the process. RNN combined with CNN performs image
classication based on image features [14, 15].
., where t is the pres-
FIGURE 9.5 RNN
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9.6.4 LSTM
LSTM utilized the concept of RNN, and it has a structure to maintain read, write,
and forget states. It reads and writes signicant information and forgets unnecessary
information. Developed by Hochreiter and Schmidhuber, the process overcame the
problem of vanishing gradient from RNN. It maintains long-term dependencies and
consists of three gates: the forget gate, the input gate, and the output gate. The forget
gate simply forgets the unwanted information, the input gate captures new information, and the output gate carries and passes the present time stamp information to
the next state. This full process is called single time stamp. Figure 9.6 presents the
single time stamp of LSTM.
LSTM networks works on sequential data so that image features were extracted
rst and combined a rchitecture of LSTM with CNN attained t he desired classication.
9.6.5 VGG-16
This DL model is based on the CNN architecture and has 16 convolution layers and
is made up of the basic constructive layers of CNN. A. Zisserman and K. Simonyan
are the developers of VGG-16.
VGG-16 has one input layer, 13 convolution layers, ve pooling layers, three fully
connected layers, and one output layer.
9.6.6 RESNET-50
ResNet-50 is a combination of CNN architecture with residual blocks. The residual
blocks help to recover the degradation problem. ResNet-50 allows the direct delivery
of data through skip connections. It acts next to the convolution and batch normalization layer. ResNet-50 captures positive values, thereby learning critical patterns
of data. The bottleneck convolution layer is special structure of ResNet and is a
collection of three convolution layers, the batch normalization layer, and the Relu
activation function. These convolution layers use 1 × 1, 3 × 3, and 1 × 1 lters,
prevent information loss, and extract dominant features from the data, and the nal
lter helps to restore the information. Skip Connections allows unchanged input to
the convolution layer output. All information is securely transferred to the next layer
without any loss. Hence it performs the deeper learning of networks.
FIGURE 9.6 LSTM gates.
119 Intelligent Deep Learning Algorithms
TruePositive + TrueNegative
A
TruePositiveTrueNegativeFalsePositiveFalseNegative+ + +
True Positive
True Positive + FalsePositive
True Positive
True Positive + FalseNegative
Precision Recall
Precision + Recall
9.7 RESULT AND DISCUSSION
This section summarizes the performance of experimented DL algorithms with
proper evaluation metrics. The evaluation metrics accuracy, recall, F1 score, and
precision are utilized to discuss the outcome of the framework.
Accuracy: It assess the true prediction from the overall given prediction.
ccuracy = (9.1)
Precision: It shows the correctly predicted positive instances from overall
positive classes.
recision =
(9.2)
Recall: It measures the correct prediction of actual positive classes.
ecall =
(9.3)
F1 score: This determines the harmonic mean between precision and recall
and also shows distribution of unequal classes.
1score = 2
*
*
(9.4)
Table 9.2 and Figure 9.7 show the performance of deep experimented models in
percentage.
Figure 9.8–9.12 demonstrate the accuracy and loss for each classication model.
Using information from Figures 9.8 to 9.12, Figure 9.13 shows the DL model classication performance.
FIGURE 9.7 Performance of deep experimented models.
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TABLE 9.2
Performance of Deep Experimented Models
Models Precision Recall F1 Score Accuracy Loss
CNN 84 89 93 95 1.63
RNN-CNN 79 85 87 90 1.67
LSTM-CNN 80 86 89 92 1.59
VGG-16 81 87 90 94 1.54
ResNet-50 87 92 97 99 1.47
FIGURE 9.8 CNN classication.
FIGURE 9.9 RNN-CNN classication.
FIGURE 9.10 LSTM-CNN classication.
FIGURE 9.11 VGG-16 classication.
121 Intelligent Deep Learning Algorithms
FIGURE 9.12 ResNet-50 classication.
FIGURE 9.13 Accuracy and loss for DL models
122 Computational Intelligence Algorithms
9.8 CONCLUSION
ASD is a silent disease that gets worse with the long-run growth of aficted children. It
totally affects the social behavior, understanding, communication, and physical activities of the children. Early identication may control the severeness of the ASD [16–18].
This chapter tries to identify the better classication algorithm for ASD facial image
recognition for autistic and nonautistic children. It proposes ve DL models for autism
detection, which include CNN, RNN-CNN, LSTM-CNN, VGG-16, and ResNet-50.
The CNN model achieves an accuracy of 95%, RNN-CNN attains an accuracy of
90%, 92% accuracy for LSTM-CNN, 94% for VGG-16, and 99% for ResNet-50. As
a result ResNet-50 attains the highest result for ASD classication. This chapter nds
that every model performs well in classication, and differences between them are
very minor, but ResNet outperformed the other models. Hence this chapter provides a
solution to identify autistic children at low-cost implementation.
9.9 FUTURE ENHANCEMENT
For ASD detection, a golden metric dataset is not available for public usage.
Upcoming researchers need a proper dataset for ASD detection. It will create a gateway to analyze various advanced architectures with AI integration.
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