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184 Computational Intelligence Algorithms
FIGURE 13.8 Comparative analysis of the proposed model’s performance against existing
models.
You et al. [16] demonstrate similar accuracies at 91.0% and 91.07%, respectively.
However, both studies have limitations, such as reliance on single-modality data,
which may affect overall performance due to data variability. Tanveer et al. [12]
achieved 85.27% accuracy, but the limited exploration of model architectures
and hyperparameters likely constrained their results. Liu et al. [14] reported an
accuracy of 88.90%, yet the single-instance cross-validation they employed may
limit the robustness of their performance estimates. Zhang et al. [19] achieved
an accuracy of 87.12%; however, their findings could have been affected by
overfitting because of the intricate structure of their CNNs. On the other hand,
with a 95.86% accuracy, the STN-DRN technique fared substantially better than
these methods. This increased accuracy implies that STN-DRN successfully
overcomes some drawbacks of alternative strategies, including enhanced generalization skills and better handling of multimodal input. These results emphasize the importance of robust model exploration, extensive data consumption,
and sophisticated validation procedures to achieve incredible classification performance in AD research.
13.4.4 EXPERIMENTAL RESULTS
The primary components of the confusion matrix are true positives (TP), true
negatives (TN), false positives (FP), and false negatives (FN). Each of these provides a distinct perspective on how the model operates. The diagonal elements
STN-DRN 185
of the matrix indicate the TP or events that were adequately anticipated. The FP
and FN, in comparison, are represented by the off-diagonal components as an
example of the improperly classified class instances. This matrix clearly shows
the model’s capacity to distinguish between different categories. The confusion matrix, shown in Figure 13.9, thoroughly examines the model’s performance in classifying objects. It is beneficial to normalize the confusion matrix
to understand the model’s performance better, especially when classes have different sample sizes. Normalizing involves dividing each cell by the sum of its
respective row, thus converting the counts into proportions. This process allows
for a more precise comparison of prediction performance across different
classes.
TP are the instances where the network effectively classifies the positive
instances. From the confusion matrix, the value 160 for “non-dementia” represents the number of cases accurately classified as “non-dementia” cases. FP are
the instances where the network incorrectly classifies the positive instances. In
this example, the value 1 in the first row and the second column indicates that
one “non-dementia” case was wrongly classified as “very mild dementia.” TN
are instances where the network effectively classifies the negative instances;
although these values are not directly shown in the matrix, they can be inferred
FIGURE 13.9 Confusion matrix of the proposed STN-DRN model.
186 Computational Intelligence Algorithms
from the total counts. FN are the instances where the network incorrectly classifies the negative instances. For example, from the confusion matrix, the value
3 in the second row and first column indicates that three “very mild dementia”
cases were misclassified as “non-dementia.” The normalized confusion matrix
is visualized using a heatmap. The normalized value of 0.97 in the first row and
first column signifies that 97% of the “non-dementia” instances were correctly
predicted as “non-dementia.” Meanwhile, the value of 0.01 in the first row and
the second column indicates that 1% of “non-dementia” instances were incorrectly predicted as “very mild dementia.” Detailed insights from the normalized confusion matrix are crucial for understanding the model’s strengths and
weaknesses, guiding improvements, and enhancing the classification model’s
performance evaluation.
When assessing the proposed model for a multiclass AD classication problem based on training and validation accuracy and training and validation loss,
it becomes evident that accuracy improves while loss decreases, as illustrated in
Figures 13.10 and 13.11.
Figure 13.12 shows the sample result obtained by the STN-DRN model.
The model’s accuracy indicates strong performance across different classes. It
is exceptionally proficient in detecting cases such as “no dementia” and “very
mild dementia.” Nonetheless, occasional misclassifications, particularly within
“moderate dementia,” highlight areas for potential enhancement. This performance summary offers valuable insights into the model’s strengths and areas
needing improvement, which could inform adjustments to boost classification
accuracy for all categories.
FIGURE 13.10 Accuracy graph of the proposed STN-DRN network.
STN-DRN 187
FIGURE 13.11 Loss graph of the proposed STN-DRN network.
FIGURE 13.12 Sample classication results of the proposed STN-DRN model.
188 Computational Intelligence Algorithms
13.5 CONCLUSION
This chapter proposes a DL model combining a deep residual network (ResNet)
model with spatial transformer networks called STN-DRN. The ResNet-101 is used
as the feature extractor, and the conventional Relu activation function is replaced
with the innovative Mish activation function. The integration of STN enables the
transformation of spatial information within MRI images of AD patients into an
alternate space while preserving crucial information. The performance of the proposed model is validated using the OASIS dataset, and the proposed model achieves
a classication accuracy of 95.86%, outperforming most existing approaches. The
performance and computational time of the proposed model can be improved in
future using a transformer network.
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Part III
Machine Learning and
AI Applications in
Neurological Disorders
Evaluation of Supervised
14
Learning Algorithms
in Detection of
Neurodisorders
A Focus on Parkinson’s
Disease
Chitigala Mouleeshwari, C. Kishor Kumar Reddy,
D. Manoj Kumar Reddy, and Srinath Doss
14.1 INTRODUCTION TO NEURODISORDERS
AND PARKINSON’S DISEASE
Neurodisorders are reviewed, with a specic focus on Parkinson’s disease (PD), in
Section 14.1. It explains the process by which neurodisorders lead to central nervous
system diseases and how such states can generate both motor and nonmotor symptoms with a striking impact on multiple features of patients’ experience of illness.
For example, Parkinson’s disease is a disorder marked by the death of neurones
in charge with producing dopamine that controls movement and manifests itself
through tremors and problems with balance. Early identication and treatment of the
disease is underscored in this section. It also details the life impact of neurodisorders
on patients’ and carers’ social, emotional, economic, and personal well-being. This
section, which by necessity mainly provides basic denitions and descriptions of the
conditions, contains no tables or gures.
14.1.1 OVERVIEW OF NEURODISORDERS
Neurological problems are those diseases that mainly inuence the main nerve systems such as the mind and spinal cord as well as the nerves in the body. They generally
can be found in several various kinds and can trigger issues with electric motor activities and also nonmotor activities such as perception, sensory processing, as well as psychological health and wellness. These problems can generally be triggered by aspects
like genes, infections, injuries, or the body’s immune system assaults. Neurological
problems are hard to take care of due to the fact that they have various signs plus intricate reasons and can seriously inuence an individual’s life coupled with the human
DO I: 10.1201/ 97810 03520 34 4 -17
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