Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

Bahaddad, Adel A.; Ragab, Mahmoud; Ashary, Ehab Bahaudien; Khalil, E. M.

doi:10.1155/2022/9276579

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…For instance, the proposed algorithm can be implemented in any place where other evolutionary algorithms such as genetic algorithm have been utilized for itemset mining as shown in [ 110 , 111 ]. Besides, it can be integrated with any deep learning algorithms to diagnose the disease as handled in [ 112 ].…”

Section: Discussionmentioning

confidence: 99%

A penalty-based algorithm proposal for engineering optimization problems

Öztaş

Erdem

2022

Neural Comput & Applic

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This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.

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Section: Discussionmentioning

confidence: 99%

A penalty-based algorithm proposal for engineering optimization problems

Öztaş

Erdem

2022

Neural Comput & Applic

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“…As depicted by an individual, it employs one of the most successful fertilization procedures for spawning new individuals in order to enhance the soil quality of the poorest region as much as possible. (10) where X MGlobal signifies the arbitrarily chosen individual in the global memory, and h 1 is computed as follows:…”

Section: Hyperparameter Tuning Using Ffamentioning

confidence: 99%

“…Each artificial neural network (ANN) contains a vast number of layers that aid in the processing of data, and each hidden layer may have a particular activation function. The hidden layer tries to reach more goals, but it is not the product's final “image” [ 10 ]. In addition to scientifically documenting therapeutic outcomes, HSV has the potential to supplement and replace clinical diagnosis of voice disorders and vocal fold vibratory dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy

Sakthivel

Prabhu

2022

Journal of Healthcare Engineering

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The use of high-speed video-endoscopy (HSV) in the study of phonatory processes linked to speech needs the precise identification of vocal fold boundaries at the time of vibration. The HSV is a unique laryngeal imaging technology that captures intracycle vocal fold vibrations at a higher frame rate without the need for auditory inputs. The HSV is also effective in identifying the vibrational characteristics of the vocal folds with an increased temporal resolution during retained phonation and flowing speech. Clinically significant vocal fold vibratory characteristics in running speech can be retrieved by creating automated algorithms for extracting HSV-based vocal fold vibration data. The best deep learning-based diagnosis and categorization of vocal fold abnormalities is due to the usage of HSV (ODL-VFDDC). The suggested ODL-VFDDC technique starts with temporal segmentation and motion correction to identify vocalized regions from the HSV recording and gathers the position of movable vocal folds across frames. The attributes gathered are fed into the deep belief network (DBN) model. Furthermore, the agricultural fertility algorithm (AFA) is used to optimize the hyperparameter tuning of the DBN model, which improves classification results. In terms of vocal fold disorder classification, the testing results demonstrated that the ODL-VFDDC technique beats the other existing methodologies. The farmland fertility algorithm (FFA) is then used to accurately determine the glottal limits of vibrating vocal folds. The suggested method has successfully tracked the speech fold boundaries across frames with minimum processing cost and high resilience to picture noise. This method gives a way to look at how the vocal folds move during a connected speech that is completely done by itself.

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“…In previous radiomics studies, the applications of deep ensemble models were relatively lacking ( 24 – 26 ). If the selected network structure and parameter settings are not appropriate, this may increase the complexity of the model and reduce its overall operating efficiency ( 27 ). Hence, the parameter optimization and layer number setting steps of deep ensemble models are still key issues that need to be solved ( 28 ).…”

Section: Introductionmentioning

confidence: 99%

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

Chen

et al. 2023

Front. Neurol.

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BackgroundEarly stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.MethodsThe research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method.ResultsAmong the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies.ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

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Metaheuristics with Deep Learning-Enabled Parkinson’s Disease Diagnosis and Classification Model

Cited by 11 publications

References 26 publications

A penalty-based algorithm proposal for engineering optimization problems

A penalty-based algorithm proposal for engineering optimization problems

Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features

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