Structural MRI classification for Alzheimer's disease detection using deep belief network

Faturrahman, Moh.; Wasito, Ito; Hanifah, Nurul; Mufidah, Ratna

doi:10.1109/icts.2017.8265643

Cited by 31 publications

(13 citation statements)

References 9 publications

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“…Voxel-based morphometric (VBM) Voxel-based morphometric which actually uses statistics for identifying deviations in brain anatomy between groups of subjects has been used in [60].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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“…Voxel-based morphometric (VBM) Voxel-based morphometric which actually uses statistics for identifying deviations in brain anatomy between groups of subjects has been used in [60].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

“…Furthermore, in [60] another approach based on DBN architecture has been proposed. Voxel-based morphometric (VBM) approach has been used for feature extraction here.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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“…In healthcare, common machine learning advances have been evolving for years. The application of AI has the capacity to assist with case triage and diagnoses [ 26 ], enhance image scanning and segmentation [ 34 ], support decision making [ 11 ], predict the risk of disease [ 35 , 36 ], and in neuroimaging [ 37 ]. Here we provide a brief overview of current advances in AI applications to specific aspects of health science.…”

Section: Ai In Healthcarementioning

confidence: 99%

Machine Learning in Healthcare

Habehh

Gohel

2021

191

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: Recent advancements in artificial intelligence (AI) and machine learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and predicting disease state and immune response, amongst a few. Although skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms, including supervised, unsupervised and reinforcement learning, along with examples. Second, we discuss the application ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare, such as system privacy and ethical concerns and provide suggestions for future applications.

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“…Mufidah et al [110] have been proposed DBN method for the classification of Alzheimer's disease (AD) based on structural modalities (MRI data). The result of this model has been compared with the support vector machine (SVM).…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Structural MRI classification for Alzheimer's disease detection using deep belief network

Cited by 31 publications

References 9 publications

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Machine Learning in Healthcare

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

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