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264 Computational Intelligence Algorithms
FIGURE 17.3 Performance of the child dataset.
The classiers are evaluated using bar charts and heat maps. A heat map is a
powerful visualization tool for comparing the performance of classiers across multiple metrics and datasets. It provides a clear and intuitive way to understand how
different classiers perform in various scenarios. A bar chart is a straightforward
and effective visualization tool for comparing the performance of classiers across
different metrics and datasets.
For the Child dataset as shown in Fig ure 17.3, the SVM stands out as the top performer across all metrics. It leads in accuracy (94.34), precision (92.23), and recall
(93.01), suggesting it is the most effective classier for this particular dataset. The
KNN algorithm follows closely behind, demonstrating strong performance but falling slightly short of the SVM’s scores. The DT, while still effective, consistently
ranks lower in all metrics, indicating it might be less suitable compared to SVM and
KNN for this dataset.
In the Adolescent dataset, SVM again shows superior performance, particularly
in precision (92.34) and recall (90.01). The results are shown in Figu re 17.4. KNN
performs competitively but does not quite reach the levels achieved by SVM, particularly in recall. The DT is less effective across all metrics, with the lowest scores
in accuracy, precision, and recall. This suggests that SVM and KNN are better suited
for handling the Adolescent dataset.
For the Adult dataset, SVM excels in all metrics, achieving the highest values
in accuracy (98.34), precision (98.01), and recall (98.67). KNN performs slightly
behind SVM, showing high scores in accuracy, precision, and recall. The DT lags
behind SVM and KNN. This indicates that SVM is the most effective classier
for the Adult dataset, with KNN also performing exceptionally well, as shown in
Figure 17.5.
Overall, SVM generally outperforms both KNN and DT in most scenarios, particularly excelling in precision and recall. KNN shows competitive results, especially in datasets with moderate to high performance, while the DT, despite being a
265 From Data to Diagnosis
FIGURE 17.4 Performance of the adolescent dataset.
robust and interpretable model, often underperforms relative to the other classiers
in these datasets.
Overall, SVM outperforms DT and KNN in terms of accuracy, precision, and
recall across all datasets. It achieves the highest accuracy with scores of 94.34% for
the Child dataset, 89.23% for the Adolescent dataset, and 98.34% for the Adult dataset. In precision, SVM again leads with 92.23% for Child, 92.34% for Adolescent,
and 98.01% for Adult. Additionally, SVM excels in recall, recording 93.01% for
Child, 90.01% for Adolescent, and 98.67% for Adult. While KNN shows strong
recall performance, especially in the Adult dataset where it matches SVM, DT falls
short, particularly in precision for the Adolescent dataset, as shown in Figure 17.6.
FIGURE 17.5 Performance of the adult dataset.
266 Computational Intelligence Algorithms
FIGURE 17.6 Performance of classiers in metrics.
In the Child dataset, SVM leads in accuracy, precision, and recall, surpassing
both DT and KNN. For the Adolescent dataset, SVM continues to dominate in all
three metrics − accuracy, precision, and recall − though KNN shows a notable gap
in precision compared to SVM. In the Adult dataset, SVM delivers the highest scores
across all metrics, including accuracy, precision, and recall. Here, DT outperforms
KNN, especially in recall, demonstrating a stronger performance relative to KNN.
For the DT classier, its notable strength lies in achieving better recall compared
to KNN in the Adult dataset. However, it generally underperforms relative to SVM
across all metrics and datasets, especially in the Adolescent and Child datasets. The
SVM classier, on the other hand, consistently excels in accuracy, precision, and
recall across all datasets, showing no weaknesses compared to the other classiers.
KNN demonstrates solid recall performance, particularly in the Adult dataset, but
tends to fall short in accuracy and precision when compared to SVM, and occasionally even lags behind DT in the Child and Adolescent datasets.
Overall, the SVM classier demonstrates superior performance in all metrics
across the datasets, making it the preferred model for this particular set of data. The
DT model shows more variability but performs well in specic cases, such as the
Adult dataset for recall. KNN, while showing decent recall, lags in accuracy and
precision compared to SVM and DT.
17.5 CONCLUSION
The importance of early detection and diagnosis in neurodisorders cannot be overstated. It is a critical factor in improving patient outcomes, managing symptoms
more effectively, and enhancing the overall quality of life. Early diagnosis not only
267 From Data to Diagnosis
provides the opportunity for timely intervention but also empowers patients and families with the information and control necessary for proactive management. It plays
a crucial role in the advancement of personalized medicine and public health, contributing to a deeper understanding of neurological diseases and the development of
more effective treatments. As research and technology continue to evolve, the ability
to detect and diagnose neurodisorders at earlier stages will remain a cornerstone of
effective healthcare and patient care. Supervised learning signicantly enhances the
diagnosis and management of neurodisorders by leveraging labeled datasets to train
algorithms for precise pattern recognition and anomaly detection. This approach
is crucial for early and accurate identication of conditions such as Alzheimer’s,
Parkinson’s, and multiple sclerosis, where timely intervention can profoundly impact
disease progression and patient quality of life. Techniques like SVM, KNN, and DT
offer valuable tools for analyzing complex neurological data and improving diagnostic accuracy. Despite challenges, particularly in diagnosing ASD, advancements
in machine learning, including personalized medicine and the integration of genetic
and neuroimaging data, promise to further rene and enhance early detection and
treatment strategies, ultimately leading to better patient outcomes and a deeper
understanding of neurodisorders. The analysis reveals that SVM consistently outperforms both KNN and DT across all datasets − children, adolescents, and adults −
in terms of accuracy, precision, and recall, demonstrating its superiority as a classier for ASD detection.
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Parkinson’s Disease
18
Detection from Drawing
Images Using Deep
Pretrained Models
Sourabh Shastri, Sachin Kumar,
and Vibhakar Mansotra
18.1 INTRODUCTION
Parkinson’s disease (PD) is an increasingly common neurological illness that impairs
both motor and nonmotor (nonmovement) abilities, hence compromising a person’s
general quality of life [1–3]. PD is the second-most predominant neurological condi-
tion. It is brought on by a breakdown of dopamine-producing neurons in the brain’s
substantia nigra [4, 5]. Several other reasons, viz. genetic factors, environmental
factors, biological factors, pathological factors, and complex interactions, also contribute to the development of Parkinsonian disorder. Although there is no single
test to detect PD, there are several techniques that can assist in identifying the illness and enhance sufferers’ quality of life. The following techniques are employed:
observation, tracking, dopamine transporter imaging (DaTscan), computed tomography (CT) scan, magnetic resonance imaging (MRI), reaction to medicine, physical and neurological tests, and clinical diagnostic criteria. PD patients may have
bradykinesia, involuntary shaking, rhythmic movements, problems with balance
and stability, and a temporary loss of the ability to begin or continue walking (gait
interruption). Conversely, PD’s nonmotor symptoms include changes in sensory perception, behavioral abnormalities, and cognitive function decits. The number of
people suffering from PD is increasing gradually and exceeding 10 million worldwide [6–8]. Therefore, the efcacy of novel medications and the quality of medical
care for PD patients depend greatly on early diagnosis [9]. To evaluate the ne motor
control and coordination in clinical assessments, spiral shape images are drawn by
patients on paper to get valuable insights, and this process is also used as a diagnostic
tool for assessing PD. These spiral-shaped images differ between healthy individuals and those who suffer from PD. To ensure that the PD patients receive prompt
care, it is necessary to have professionals evaluate the drawings of both groups as
soon as possible [10–12]. Deep learning (DL) is widely used for diagnosing diseases
and achieves high-performance results by utilizing voluminous medical data and
complex computational models. By using the DL models, the classication of spiral
DO I: 10.1201/ 97810 03520 34 4 -21
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270 Computational Intelligence Algorithms
shape images drawn by the individuals having PD symptoms and those who are
healthy becomes quite easy and assists healthcare professionals and experts in the
early diagnosis of PD disorder among individuals [13–17]. The present study compares the performance measures of the six pretrained DL models − VGG16, VGG19,
DenseNet121, DenseNet169, InceptionNetV3, and Xception − in terms of their ability to classify both healthy groups and PD patients.
The present work has made the following principal contributions:
i. The proposed study reduces the likelihood of misdiagnosis and helps in the
early identication of Parkinson’s illness thanks to DL’s processing power.
ii. The work offers a comprehensive performance analysis of these various
deep pretrained models on PD data.
iii. The study assists medical practitioners in early PD diagnosis using the best-
performed model.
The present work is organized into six sections, beginning from the earlier research in Section 18.2 followed by material and methods in Section 18.3.
Additionally, Section 18.4 presents the experiments and ndings, and Sections 18.5
and 18.6 explain the current work’s discussion and conclusion, respectively.
18.2 LITERATURE REVIEW
This section discusses several studies to detect Parkinsonian disorder using various
state-of-the-art methods. Researchers from different parts of the world used different methods for diagnosing PD, including MRI scans, speech and gait signals,
electroencephalogram (EEG) and electromyography (EMG) signals, and singlephoton emission computerized tomography (SPECT) scans. In addition, researchers
have also proposed various methods of diagnosing PD with the help of handwritten
images, especially spiral drawings. Several aspects, including kinematic, geometrical, entropic, energetic, temporal, spectral, and nonlinear features, were extracted
from the raw datasets using graphical tablets, which were used to analyze handwriting samples. To ascertain the state of PD, several preprocessing, feature selection, and supervised learning strategies have been used in conjunction with machine
and DL techniques. [18] worked to diagnose PD early by estimating the changes in
handwriting. The dataset about handwritten spirals drawn by the PD patients has
been utilized and kinematic features have been extracted from the same. They used
XGBoost, AdaBoost, random forest (RF), and support vector machine (SVM) as
their four classiers. With the mutual information gain feature selection approach
used, the AdaBoost algorithm fared better than the other algorithms, achieving
scores of 96.02%, 91.93%, 100.00%, 100.00%, and 95.79% for sensitivity, accuracy,
and precision, respectively. In another study [19], authors used an equal amount of
data about spiral and sinusoidal handwritten drawings of PD patients and normal
individuals for identifying one of the cardinal signs of PD, i.e., tremor detection.
Analogously, [20] worked on digitized spiral drawings and extracted in-air and
on-surface kinematic features using mathematical models. Four machine learning
(ML) algorithms, random forest, K-nearest neighbor (KNN), SVM, and logistic
271 Parkinson’s Disease Detection
regression, were used to identify PD. Using random forest and logistic regression
among others, 91.6% accuracy was obtained. An attempt was made by [21] to diagnose PD at an early stage by differentiating between PD patients and healthy controls using convolutional neural network (CNN) architecture. A total of 87 subjects
comprising 58 PD patients and 29 healthy controls of the same age were engaged
to draw wire cubes and spiral pentagons, and it was concluded that these two tests
have almost the same ability to differentiate PD patients and healthy controls. Two
distinct hand-drawn data patterns, spiral, and wave, have been used by [22] for early
detection of PD wherein six pretrained models, viz. VGG16, VGG19, ResNet18,
ResNet50, ResNet101, and Vit were used. Each of these models was assessed based
on three performance criteria: accuracy, precision, and F1 score. Together, the
VGG19 model and the recommended model produced the best average accuracy of
96.67% out of all of them. In another study on PD using handwriting-balanced data
from 42 subjects, [23] proposed an automatic classication system by applying CNN
and CNN−bidirectional long short-term memory (CNN-BLSTM) for PD detection.
The CNN-BLSTM model, which was trained utilizing jittering and synthetic data
augmentation approaches, had the best results, with an accuracy of 97.62%. It has
been proposed to stop the progression of PD by developing an early automated diagnosis technique for the treatment of symptoms using several handwriting datasets
and deep transfer learning-based algorithms [24]. When paired with CNN ne-tuned
architectures, the usage of data-augmented pictures yields the greatest results, with
99.22% accuracy. A novel method has been proposed by [25] based on the segmentation of online handwritten text into lines. PD early identication has been achieved
by using the temporal and spectral characteristics of Arabic online handwriting.
Three classiers that are KNN, SVM, and decision tree (DT) as well as a stratied
nested ten-cross-validation were used for the experiments. Of the three classiers,
DT provided the greatest accuracy, at 92.86%. In the same direction, [26] worked
on two publicly accessible datasets including PaHaW and NewHandPD of sequencebased dynamic handwriting for the early diagnosis of PD using the combination of
one-dimensional (1D) convolutions and bidirectional-gated recurrent unit (Bi-GRU)
layers for the classication purpose. The NewHandPD handwriting dataset has been
used for the accurate detection of PD by utilizing transfer learning models such as
ResNet50, VGG19, and InceptionV3 along with the optimization algorithm, viz. the
genetic algorithm, by [27]. The proposed model provided an accuracy of 95.29%,
recall of 0.86, precision of 0.98, and area under the curve (AUC) of 0.90.
One of the important symptoms of PD is gait abnormality, i.e., unusual walking
patterns, and [28] have worked to build a model for analyzing gait data for the detection of PD. For this, a 1D CNN has been proposed and worked on 166 subjects (93
PD patients and 73 normal). The detection of abnormalities in gait has been obtained
with an accuracy of 98.7%, and the prediction of a subject’s Unied Parkinson’s
Disease Rating Scale (UPDRS) severity has been achieved with an accuracy of
85.3%. The voice measurements dataset used is available to the public via UCI. The
static and dynamic features of the speech-related dataset of 45 subjects about PD
have been studied by [29] and bi-directional LSTM has been used for capturing
time-series dynamic features. The results of the study were found have been better
than those of previous similar works of ML using static features. As PD progresses
272 Computational Intelligence Algorithms
due to the deciency of dopamine, [30] used MRI images that capture the structural
changes in the brain. The images of patients suffering from PD and normal subjects
have been trained by using AlexNet DL architecture and tested for evaluating its
performance. [31] used MRI images for the classication of healthy individuals and
patients suffering from PD using transfer learning techniques and data augmentation. The original data were increased by generative adversarial network (GAN), and
pretrained Alex-Net has been utilized for the classication purpose.
18.3 MATERIALS AND METHODS
The study’s dataset, the DL pretrained models that were utilized for analysis, and the
suggested research methods are all described in this section.
18.3.1 DATASET DESCRIPTION
The dataset that this research used includes 204 spiral and wave drawing pictures
that were obtained from [12], which contains a total of 204 spiral and wave drawing
images. These images are evenly divided between patients and healthy/normal individuals diagnosed with PD, with each class containing 102 images. The drawings
are used to evaluate the motor symptoms associated with PD, as the disorder often
affects ne motor skills. For this study, the images from the dataset were resized to
224 × 224 pixels to meet the input requirements of the DL models. The dataset was
split into training and testing sets using an 80:20 ratio (80% of the images used for
training and 20% for evaluating model performance). The distribution chart of data
is shown in Figure 18.1, and sample images of spiral and wave drawing are shown
in Figure 18.2.
18.3.2 DEEP LEARNING PRETRAINED MODELS
In this work, drawing images of people with PD and healthy people are distinguished
from each other using six pretrained DL models: VGG16, VGG19, DenseNet121,
DenseNet169, InceptionNetV3, and Xception. The VGG architecture serves as the foundation for both VGG16 and VGG19, with VGG19 having a deeper network structure.
Both are suited for transfer learning since they make use of tiny 3 × 3 convolutional lters and were pretrained on the ImageNet dataset. DenseNet121 and DenseNet169 have
designs with dense connections that improve gradient ow and feature reuse. Their 121
and 169 layers, respectively, enable them to record intricate patterns that are essential
for recognizing motor impairments associated with PD in drawings. InceptionNetV3
boosts computing performance by utilizing techniques like label smoothing and factorized convolutions. Xception improves on the Inception architecture and boosts its
capacity to extract ne-grained data by utilizing depthwise separable convolutions. All
models use their pretrained data from ImageNet to enhance the classication of drawings to detect PD. By examining several models, the study seeks to determine which
architecture is most suitable for this task.
VGG16 model consists of 16 learnable weight layers, including three fully con-
nected layers and 13 convolutional layers. Small 3 × 3 lters are used in each
273 Parkinson’s Disease Detection
FIGURE 18.1 Data distribution plot.
FIGURE 18.2 Sample images from the dataset.
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