Study on the Performance of Deep Learning Techniques for the Classification of Parkinson’s Diseases
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Date
2024-02
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Avinashilingam
Abstract
Machine Learning and Deep Learning are promising technologies that have the
potential to assist and support clinicians in providing an objective and reliable diagnosis.
These technologies play a crucial role in analyzing vocal features for the early detection
and monitoring of Parkinson's disease. The advancements in these fields aim to furnish
objective and quantitative measures of vocal impairments, thereby offering valuable
insights for both diagnosis and the evaluation of treatment outcomes.
Parkinson's disease (PD) is a progressive neurodegenerative disorder that
primarily affects movement and is characterized by a range of motor and non-motor
symptoms. One notable non-motor symptom is vocal impairment, which can significantly
impact communication and quality of life for individuals with PD. The manifestation of
vocal impairments in Parkinson's disease is commonly referred to as hypokinetic
dysarthria. Vocal features are increasingly recognized as important diagnostic markers for
Parkinson's disease. Assessment of vocal impairments, along with other motor and non-
motor symptoms, contributes to a comprehensive diagnosis.
The main goal of this research work is to develop an accurate and robust
classification model for Parkinson's disease (PD) by employing deep learning techniques,
specifically focusing on vocal features. The study aims to achieve the objectives through
Feature extraction, Dimensionality reduction & feature ranking, Feature Selection using
optimisation techniques, Ensemble feature selection methods and Ensemble Deep
Learning classifiers.Initially, feature extraction technique is implemented to extract Vocal fold,
TQWT,WT, MFCC, Time frequency and baseline features were extracted from dataset.
Dimensions are reduced by introducing Kernel based Principal Component Analysis
(KPCA).It attempts to find a linear subspace of lower dimensionality than the original
sound recording feature space, where the new sound recording features of PD have the
largest variance. Minimum Redundancy Maximum Relevance (mRMR) technique is
introduced for selecting informative features. High relevance score based on class label are
selected and redundant features are eliminated. Finally, Fuzzy Convolution Long Short-
Term Memory based Convolutional Neural Network (FCLSTM-CNN) classifier is
introduced for PD classification. Here, triangular membership function is used for burning
bias and weight values. In the mRMR algorithm, NP-hard problems will occur and it can
be solved by using the Swam Intelligence methods.
In the second phase of the study, the Fuzzy Monarch Butterfly Optimization
Algorithm (FMBOA) is implemented to effectively select crucial features from the
dataset, thereby improving the Parkinson's Disease (PD) detection rate. In this phase, the
weight value plays a crucial role in the quest for optimal features within the
dimensionality-reduced feature set. The computation of the weight value involves the use
of the Gaussian fuzzy membership function, contributing to the algorithm's enhanced
performance. Various classification algorithms are employed to evaluate different feature
sets derived from FMBOA, each exhibiting distinct combinations. The proposed Fuzzy
Convolution Bi-Directional Long Short-Term Memory (FCBi-LSTM) classifier is
introduced for PD classification. This innovative classifier amalgamates multiple speech
feature types at the feature level, facilitating the identification of individuals with PD in
contrast to those without. Furthermore, there is potential for extending this work to
incorporate the Ensemble Feature Selection algorithm to enhance overall accuracy.
In the third phase, Optimization Based Ensemble Feature Selection (OBEFS)
algorithm has been proposed to select features based on the consensus. OBEFS algorithm
is introduced to combine three methods such as Fuzzy Monarch Butterfly Optimization
Algorithm (FMBOA), Levy Flight Cuckoo Search Algorithm (LFCSA), and Adaptive
Firefly Algorithm (AFA). Correlation function is introduced to combine the results of
methods in order to select the optimal features. The proposed OBEFS algorithm yields
better results for three feature subsets than other combinations of feature sets. The optimal
features selected through OBEFS algorithm is used to train a Fuzzy Convolutional Bi-
Directional Long Short-Term Memory (FCBi-LSTM) classifier. This can be extended to
ensemble learning. Ensemble learning evaluates a variety of approaches instead of single
classification algorithms, and final results are generated by merging outputs of classifiers.
In the final phase, Ensemble Deep Learning (EDL) classifiers are considered for
the classification of Parkinson’s disease. EDL include FCBi-LSTM, Contractive Auto-
encoder (CAE), and Sparse Auto-encoder (SAE). CAEs represent robust adaptations of
standard autoencoders, demonstrating an ability to acquire reduced sensitivity to minor
variations in data. SAEs play a pivotal role in training Neural Network (NN) classifiers
aimed at identifying Parkinson's Disease (PD) within datasets. The amalgamation of
results from Deep Learning classifiers is achieved through the application of stacked
generalization. Enhanced predictive performance is observed in comparison to utilizing a
single model when employing Ensemble Deep Learning (EDL) techniques. The proposed
classifier undergoes experimentation and evaluation using a dataset sourced from the
University of California-Irvine (UCI) Machine Learning repository.
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Computer Science and Engineering