Attention-based 3D Convolutional Network for Alzheimer’s Disease Diagnosis and Biomarkers Exploration

Jin, Depeng; Xu, Jian; Zhao, Kun; Hu, Fangzhou; Yang, Zhengyi; Liu, Bing; Jiang, Tianzi; Liu, Yong

doi:10.1109/isbi.2019.8759455

Cited by 60 publications

(47 citation statements)

References 17 publications

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“…A classification strategy using CNN for the automatic detection of patients suffering from AD, sMCI, and cMCI was proposed in [75], where high levels of accuracy were obtained for all the different classes. A new technique termed "attention based" 3D ResNet for diagnosing AD by identifying chief brain regions associated with AD symptoms was proposed in [76]. The attention seeking protocol resulted in 92% accuracy which would rather be 90% without it.…”

Section: ) Dl-based Approaches In Ad 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: ) Dl-based Approaches In Ad Diagnosismentioning

confidence: 99%

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

et al. 2021

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“…When looking for specific architectures, we noticed that there were not many entries that used the DenseNet architecture. We did encounter papers with other varieties of residual neural networks: three-dimensional VoxResNet [ 12 ], ResNet [ 13 , 14 , 15 ], and three-dimensional ResNet [ 16 ]. Only three documents used DenseNets, of which two [ 17 , 18 ] were three-dimensional, but with depthless DenseNets, and one [ 19 ] used deep DenseNets, but two-dimensional.…”

Section: Previous Workmentioning

confidence: 99%

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Solano-Rojas

Ricardo

2021

Sensors

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Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

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“…An efficient approach to accurately predict brain conditions is by classifying MRI scans, but this task is also challenging. Nonetheless, novel approaches have been proposed to diagnosis AD at its early stages through the efficient classification of brain MRI images and the use of label propagation with convolutional neural network (CNNs) [14]. As reported by the Alzheimer's Association in 2019, treatment for AD remain unavailable.…”

Section: A Alzheimer's Diseasementioning

confidence: 99%

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

2020

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The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages. INDEX TERMS Alzheimer's disease, deep learning, early stage detection and diagnosis.

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Attention-based 3D Convolutional Network for Alzheimer’s Disease Diagnosis and Biomarkers Exploration

Cited by 60 publications

References 17 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

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

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