Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine

Bi, Xin; Zhao, Xibin; Huang, Hong; Chen, Deyang; Ma, Yuliang

doi:10.1007/s12559-019-09688-2

Cited by 65 publications

(41 citation statements)

References 39 publications

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“…DBN [ 73 ], in contrast to the DBM, is formed by stacking several RBMs together in a way that one RBM’s latent layer is linked to the next RBM’s visible layer. As the top two layers of DBN are undirected, the connections are downward directed to its immediate lower layer [ 73 , 74 ]. Thus, the DBN is a hybrid model with the first two layers as a undirected graphical model and the rest being directed generative model.…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

“…DBM [ 98 ] and RBM [ 99 ] were used in detecting AD and mild cognitive impairment (MCI) from MRI and Positron Emission Tomography (PET) scans. Again, CNN was used on MRI to detect neuroendocrine carcinoma [ 55 , 74 , 105 ]. CNN’s dual pathway version was used by Kamnitsas et al to segment lesions related to tumours, traumatic injuries, and ischemic strokes [ 109 ].…”

Section: Deep Learning and Biological Datamentioning

confidence: 99%

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Deep Learning in Mining Biological Data

et al. 2021

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Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

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Section: Overview Of Deep Learningmentioning

confidence: 99%

Section: Deep Learning and Biological Datamentioning

confidence: 99%

Deep Learning in Mining Biological Data

et al. 2021

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“…An automatic prediction approach using unsupervised DL was proposed in [95], where unsupervised CNN was used for feature extraction and an unsupervised classifier was used to take the final decision for classifying patients with AD from MCI. A brain network classification problem for identifying AD utilizing two DL methods was studied in [96], where deep regional-connectivity and adjacent positional features were learned by convolutional and recurrent learning respectively. Finally, to improve the ability of learning, the ELM-boosted structure was implemented.…”

Section: ) Dl-based Approaches In Ad Diagnosismentioning

confidence: 99%

“…Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's. PD can be diagnosed early by monitoring several symptoms including bradykinesia (slowness of movement), rigidity (stiffness of muscles rendering a person unable to stretch muscles properly), tremor at rest (shaking of body parts especially hands when at rest), and voice impairment (losing control over speech) [96], [146]. According to the category of symptoms, different ML approaches for detecting PD have been developed.…”

Section: Parkinson's Disease (Pd)mentioning

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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“…Extreme Learning Machine (ELM) [8], [9] achieves extremely fast learning speed and good generalization performance. Many variants of ELM have been developed aiming at different training strategies and scenarios, e.g., graph data learning [27], online sequential learning [28], medical data learning [29], kernelized learning [30], [31], text classification [32], distributed learning based on MapReduce [33]- [35].…”

Section: B Brief Of Extreme Learning Machinementioning

confidence: 99%

CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

Zhang

Zhao

et al. 2020

IEEE Access

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Classification over data streams is a crucial task of explosive social stream mining and computing. Efficient learning techniques provide high-quality services in the aspect of content distribution and event browsing. Due to the concept drift and concept evolution in data streams, the classification performance degrades drastically over time. Many existing methods utilize supervised and unsupervised learning strategies. However, supervised strategies require labeled emerging records to update the classifiers, which is unfeasible to work in the practical social stream applications. Although unsupervised strategies are popularly applied to detect concept evolution, it takes tremendous run-time computation cost to run online clustering. To this end, in this paper, we address these major challenges of social stream classification by proposing an efficient incremental semi-supervised classification method named CODES (Classification Over Drifting and Evolving Stream). The proposed CODES method consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module. Thus, in the drifting and evolving social streams, CODES is able to provide: 1) semi-supervised learning ability to eliminate dependency on the labels of emerging records; 2) fast incremental learning with real-time update ability to tackle concept drift; 3) efficient novel class detection ability to tackle concept evolution. Extensive experiments are conducted on several real-world datasets. The results indicate a higher performance than several state-of-the-art methods. CODES achieves efficient learning performance over drifting and evolving social streams, which improves practical significance in the real-world social stream applications. INDEX TERMS Social stream, incremental learning, semi-supervised learning, extreme learning machine.

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Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine

Cited by 65 publications

References 39 publications

Deep Learning in Mining Biological Data

Deep Learning in Mining Biological Data

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

CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

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