Unified deep learning approach for prediction of Parkinson's disease

Wingate, James; Kollia, Ilianna; Bidaut, Luc; Kollias, Stefanos

doi:10.1049/iet-ipr.2019.1526

Cited by 56 publications

(31 citation statements)

References 32 publications

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“…The results of the study showed how prediction accuracy of UPDRS increases as DL is aided by cluster analysis. A DL approach by combining CNN and RNN was investigated in [162] and a high prediction accuracy was obtained to classify PD from non-PD. A homeenvironment friendly IMU sensor based system for detecting freezing of gait was investigated in [163].…”

Section: ) Ml-based Approaches In Pd Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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Section: ) Ml-based Approaches In Pd Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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“…Recent research has focused on extracting trained DNN representations and using them for classification purposes [4], either by an auto-encoder methodology, or by monitoring neuron outputs in the convolutional or/and fully connected network layers [15,34]. Such developments are exploited in this paper, combined with clustering, for diagnosis of diseases based on medical imaging.…”

Section: Related Workmentioning

confidence: 99%

Transparent Adaptation in Deep Medical Image Diagnosis

Kollias

Vlaxos

Seferis

et al. 2021

Lecture Notes in Computer Science

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The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, transparent way for prediction in medical imaging. A novel methodology is presented, in which deep neural architectures that have been trained to provide highly accurate predictions over existing datasets are adapted, in a consistent way, to make predictions over different contexts and datasets. Unified prediction is then achieved over the original and the new datasets. Successful application is illustrated through a large experimental study for prediction of Parkinson's disease from MRI and DaTScans, as well as for prediction of COVID-19 from CT scans and X-rays.

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“…The extracted features are used to construct a deep learning model, which is very likely to accelerate the convergence speed of model training, reduce the demand for training data, and improve model performance ( Liu et al, 2018 ). On the other hand, the large data training samples required for deep learning and the large number of parameters required for model adjustment can easily lead to a much higher computational complexity and the amount of data required for training models than traditional machine learning models ( Jha and Kwon, 2017 ; Huang et al, 2020 ; Wingate et al, 2020 ). Therefore, although current neuroimaging databases have been well developed, their scale may limit the performance of deep learning to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

Zhou

et al. 2021

Front. Neurosci.

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Alzheimer disease (AD) is mainly manifested as insidious onset, chronic progressive cognitive decline and non-cognitive neuropsychiatric symptoms, which seriously affects the quality of life of the elderly and causes a very large burden on society and families. This paper uses graph theory to analyze the constructed brain network, and extracts the node degree, node efficiency, and node betweenness centrality parameters of the two modal brain networks. The T test method is used to analyze the difference of graph theory parameters between normal people and AD patients, and brain regions with significant differences in graph theory parameters are selected as brain network features. By analyzing the calculation principles of the conventional convolutional layer and the depth separable convolution unit, the computational complexity of them is compared. The depth separable convolution unit decomposes the traditional convolution process into spatial convolution for feature extraction and point convolution for feature combination, which greatly reduces the number of multiplication and addition operations in the convolution process, while still being able to obtain comparisons. Aiming at the special convolution structure of the depth separable convolution unit, this paper proposes a channel pruning method based on the convolution structure and explains its pruning process. Multimodal neuroimaging can provide complete information for the quantification of Alzheimer’s disease. This paper proposes a cascaded three-dimensional neural network framework based on single-modal and multi-modal images, using MRI and PET images to distinguish AD and MCI from normal samples. Multiple three-dimensional CNN networks are used to extract recognizable information in local image blocks. The high-level two-dimensional CNN network fuses multi-modal features and selects the features of discriminative regions to perform quantitative predictions on samples. The algorithm proposed in this paper can automatically extract and fuse the features of multi-modality and multi-regions layer by layer, and the visual analysis results show that the abnormally changed regions affected by Alzheimer’s disease provide important information for clinical quantification.

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Unified deep learning approach for prediction of Parkinson's disease

Cited by 56 publications

References 32 publications

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Transparent Adaptation in Deep Medical Image Diagnosis

Quantification of Cognitive Function in Alzheimer’s Disease Based on Deep Learning

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