Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
State-of-the-Art Machine Learning Techniques for Diagnosis of Alzheimer’s Disease from MR-Images: A Systematic Review
https://orcid.org/0000-0003-0035-5576
Abstract Alzheimer's disease (AD) is the type of dementia that affects the world's significant population and is expected to be increased worldwide. In recent years, the main focus of researchers is the early diagnosis of AD because of the unavailability of well-known causes and no permanent curative solution for AD-infected patients. A plethora of machine learning algorithms are utilized by various researchers to provide an accurate diagnosis of the patients who fall under the category of Healthy Controls (HC), Mild Cognitive Impairment (MCI), and AD. In this paper, many machine learning algorithms with a special focus on various feature extraction and fusion techniques are explored in detail, which are used by the researchers to investigate various categories of AD from the Magnetic Resonance Images (MRI). Therefore, a systematic review of different feature extraction methods such as statistical, spatial-based, and transform-based is presented in this paper. Further, various open-source standard datasets that researchers used to detect AD from medical images are also discussed in detail. As some of the feature extraction techniques mentioned above generate redundant and large feature sets that significantly affect the classification accuracy, therefore, in this paper, we have also discussed several dimensionality reduction methods available for the extraction of relevant features from medical images. Moreover, future directions are also provided to researchers to aid in the early detection of brain disorders which may be helpful to add productive years to the lives of AD-contaminated patients.
State-of-the-Art Machine Learning Techniques for Diagnosis of Alzheimer’s Disease from MR-Images: A Systematic Review
https://orcid.org/0000-0003-0035-5576
Abstract Alzheimer's disease (AD) is the type of dementia that affects the world's significant population and is expected to be increased worldwide. In recent years, the main focus of researchers is the early diagnosis of AD because of the unavailability of well-known causes and no permanent curative solution for AD-infected patients. A plethora of machine learning algorithms are utilized by various researchers to provide an accurate diagnosis of the patients who fall under the category of Healthy Controls (HC), Mild Cognitive Impairment (MCI), and AD. In this paper, many machine learning algorithms with a special focus on various feature extraction and fusion techniques are explored in detail, which are used by the researchers to investigate various categories of AD from the Magnetic Resonance Images (MRI). Therefore, a systematic review of different feature extraction methods such as statistical, spatial-based, and transform-based is presented in this paper. Further, various open-source standard datasets that researchers used to detect AD from medical images are also discussed in detail. As some of the feature extraction techniques mentioned above generate redundant and large feature sets that significantly affect the classification accuracy, therefore, in this paper, we have also discussed several dimensionality reduction methods available for the extraction of relevant features from medical images. Moreover, future directions are also provided to researchers to aid in the early detection of brain disorders which may be helpful to add productive years to the lives of AD-contaminated patients.
State-of-the-Art Machine Learning Techniques for Diagnosis of Alzheimer’s Disease from MR-Images: A Systematic Review
https://orcid.org/0000-0003-0035-5576
Abstract Alzheimer's disease (AD) is the type of dementia that affects the world's significant population and is expected to be increased worldwide. In recent years, the main focus of researchers is the early diagnosis of AD because of the unavailability of well-known causes and no permanent curative solution for AD-infected patients. A plethora of machine learning algorithms are utilized by various researchers to provide an accurate diagnosis of the patients who fall under the category of Healthy Controls (HC), Mild Cognitive Impairment (MCI), and AD. In this paper, many machine learning algorithms with a special focus on various feature extraction and fusion techniques are explored in detail, which are used by the researchers to investigate various categories of AD from the Magnetic Resonance Images (MRI). Therefore, a systematic review of different feature extraction methods such as statistical, spatial-based, and transform-based is presented in this paper. Further, various open-source standard datasets that researchers used to detect AD from medical images are also discussed in detail. As some of the feature extraction techniques mentioned above generate redundant and large feature sets that significantly affect the classification accuracy, therefore, in this paper, we have also discussed several dimensionality reduction methods available for the extraction of relevant features from medical images. Moreover, future directions are also provided to researchers to aid in the early detection of brain disorders which may be helpful to add productive years to the lives of AD-contaminated patients.
State-of-the-Art Machine Learning Techniques for Diagnosis of Alzheimer’s Disease from MR-Images: A Systematic Review
Goyal, Palak (Autor:in) / Rani, Rinkle (Autor:in) / Singh, Karamjeet (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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