A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data

Haq, Amin Ul; Li, Jian Ping; Agbley, Bless Lord Y.; Mawuli, Cobbinah Bernard; Ali, Zafar; Nazir, Shah; Din, Salah Ud

doi:10.1016/j.eswa.2022.118045

Cited by 32 publications

(9 citation statements)

References 87 publications

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“…Artificial intelligence (AI) based computer automatic diagnostic systems (CAD) can effectively different diagnose diseases in IoT healthcare system. Deep learning techniques are widely used in CAD systems to diagnose critical diseases 32 , especially convolutional neural networks. The CNN model is mostly used for medical image classification 18 , 19 .…”

Section: Discussionmentioning

confidence: 99%

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

Haq

Khan

et al. 2022

Sci Rep

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The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

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Section: Discussionmentioning

confidence: 99%

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

Haq

Khan

et al. 2022

Sci Rep

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“…Computational complexity (CC) is one of the performance metrics to evaluate to what extent one DL-based method is complicated. Moreover, it measures how many computational resources are needed to run this approach [55]. In this regard, we assess the complexity with three criteria: mathematical modeling, number of parameters, and run time.…”

Section: ) Computational Complexity and Latency Analysismentioning

confidence: 99%

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

et al. 2022

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In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

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“…Several research studies have been conducted regarding using artificial intelligence in medicine and healthcare [5][6][7][8]. Recently, more and more academic researchers are attempting to use different deep learning algorithms in the classification tasks of detecting Parkinson's [9][10][11][12]. Haller et al [13] presented a system that can help detect PD using magnetic resonance images (MRI) because it is a neurodegenerative disease that probably affects brain regions.…”

Section: Introductionmentioning

confidence: 99%

Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques

Huang,

Chaturvedi,

Nayan

et al. 2024

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Parkinson’s disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%.

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A survey of deep learning techniques based Parkinson’s disease recognition methods employing clinical data

Cited by 32 publications

References 87 publications

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques

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