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Classification of Magnetic Resonance Images using Bag of Features for Detecting Dementia

Deepika Bansal, Kavita Khanna, Rita Chhikara, Rakesh Kumar Dua, Rajeev Malhotra
2020 Procedia Computer Science  
In this paper, a model is presented for classification of Dementia brain disease using magnetic resonance imaging.  ...  The BOF approach clubbed with SVM leads to an accuracy of 93%. Abstract In this paper, a model is presented for classification of Dementia brain disease using magnetic resonance imaging.  ...  Acknowledgements The research was funded by Department of Science and Technology DST, New Delhi, Reference number DST/CSRI/2017/215 (G).  ... 
doi:10.1016/j.procs.2020.03.190 fatcat:ntkkstl7gra6zetgimxblfpynq

MRI based Techniques for Detection of Alzheimer: A Survey

Ruaa Adeeb
2017 International Journal of Computer Applications  
Alzheimer's disease(AD) is a neurological disease. It affects memory of the patient. The livelihood of the people that are diagnosed with AD.  ...  Magnetic resonance imaging (MRI) is one of the most commonly used imaging modality for the diagnosis of Alzheimer's.  ...  We further clustered descriptors of each ROI of all MRI images to form a 50-dimensional bag-of-words for each ROI.  ... 
doi:10.5120/ijca2017912929 fatcat:yni6h3pgynbujl22kliiuxqcn4

Detection of Alzheimer's disease in MRI images using different transfer learning models and improving the classification accuracy

M. Rajendiran, K. P. Sanal Kumar, S. Anu H. Nair
2022 International Journal of Health Sciences  
Various automated technologies and techniques have been developed in recent years for the detection of Alzheimer's disease (AD).  ...  Alzheimer's disease (AD) is a neurodegenerative illness that damages brain cells and impairs a patient's memory over time.  ...  An effective whole-brain hierarchical network was built by Liu J [24] for MRI-based categorization of Alzheimer's disease (AD).  ... 
doi:10.53730/ijhs.v6ns3.8944 fatcat:6q2mtfssozebxeraftlhywnsry

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

Seyed Hani Hojjati, Abbas Babajani-Feremi, the Alzheimer's Disease Neuroimaging Initiative
2022 Frontiers in Computational Neuroscience  
In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research.Objective: In this study, we aimed  ...  We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance  ...  Convolutional neural network-based MR image analysis for Alzheimer’s Disease classification. Curr. Med.  ... 
doi:10.3389/fncom.2021.769982 pmid:35069161 pmcid:PMC8770936 fatcat:lurmwqdvfrd2lmwiy7gxfmnrcq

Semantic Feature Extraction Using SBERT for Dementia Detection

Yamanki Santander-Cruz, Sebastián Salazar-Colores, Wilfrido Jacobo Paredes-García, Humberto Guendulain-Arenas, Saúl Tovar-Arriaga
2022 Brain Sciences  
Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer's disease, provided  ...  Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia.  ...  From the tokenization of words, it is feasible to perform a word bag model in which all the words used throughout the text are identified.  ... 
doi:10.3390/brainsci12020270 pmid:35204032 pmcid:PMC8870383 fatcat:v7nmssuzonen7avrb4lnstmjea

Stacked Deep Dense Neural Network Model to Predict Alzheimer's Dementia Using Audio Transcript Data

Yusera Farooq Khan, Baijnath Kaushik, Mohammad Khalid Imam Rahmani, Md Ezaz Ahmed
2022 IEEE Access  
Network (SDDNN) model for text classification and prediction of Alzheimer's dementia.  ...  Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia.  ...  CONFLICTS OF INTEREST The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.1109/access.2022.3161749 fatcat:mz3avhjztjdo3ojlnp2qsw5voa

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Zhao Zhang, Guangfei Li, Yong Xu, Xiaoying Tang
2021 Diagnostics  
Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer's disease (AD), Parkinson's disease (PD), major depressive disorder (MDD  ...  An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can  ...  Applications in Human Brain MRI Image Classification Tasks Alzheimer's Disease Alzheimer′s disease is a neurodegenerative disease with a slow onset and w over time.  ... 
doi:10.3390/diagnostics11081402 fatcat:mmouz5fb2ngzbe7jj2fyi5xpsy

Online Signature Analysis for Characterizing Early Stage Alzheimer's Disease: A Feasibility Study

Zelong Wang, Majd Abazid, Nesma Houmani, Sonia Garcia-Salicetti, Anne-Sophie Rigaud
2019 Entropy  
We aimed to explore the online signature modality for characterizing early-stage Alzheimer's disease (AD).  ...  We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e21100956 fatcat:ph53crm5vvb6vc4tjg3riouiei

Radiological images and machine learning: Trends, perspectives, and prospects

Zhenwei Zhang, Ervin Sejdić
2019 Computers in Biology and Medicine  
By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.  ...  In many applications, machine learning based systems have shown comparable performance to human decision-making.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  ... 
doi:10.1016/j.compbiomed.2019.02.017 pmid:31054502 pmcid:PMC6531364 fatcat:tcyorm6g3ff6dg7ty2ubtqorjq

Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles

Hyun-Soo Choi, Jin Yeong Choe, Hanjoo Kim, Ji Won Han, Yeon Kyung Chi, Kayoung Kim, Jongwoo Hong, Taehyun Kim, Tae Hui Kim, Sungroh Yoon, Ki Woong Kim
2018 BMC Geriatrics  
The key idea of the proposed framework is to propose a cost-effective and precise two-stage classification procedure that employed Mini Mental Status Examination (MMSE) as a screening test and the KLOSCAD  ...  The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abundance of information.  ...  dementia type classification (Alzheimer's disease versus vascular dementia versus dementia with Lewy bodies, and so on).  ... 
doi:10.1186/s12877-018-0915-z pmid:30285646 pmcid:PMC6171238 fatcat:mydphoqov5hhppvbnv364sno34

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Michael Lee, Ting Hu
2019 Metabolites  
Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle.  ...  Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state.  ...  The parameters of the ANN are then updated starting from the output layer to each predecessor layer based on the gradient of the cost function.  ... 
doi:10.3390/metabo9040066 pmid:30987289 pmcid:PMC6523328 fatcat:iluqijb2pvgytkdc2m4gjmdtia

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review

Kristy A Carpenter, Xudong Huang
2018 Current pharmaceutical design  
The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery.  ...  subset of ANN that utilize convolution.  ...  In this section, we conceptualize a workflow for applying ML-based VS to the search for potential therapeutic agents for Alzheimer's disease (AD).  ... 
doi:10.2174/1381612824666180607124038 pmid:29879881 pmcid:PMC6327115 fatcat:kvowpmifvjhpblsqbgkquamwmi

A Machine Learning Approach towards Detecting Dementia based on its Modifiable Risk Factors

Reem Bin-Hezam, Tomas E.
2019 International Journal of Advanced Computer Science and Applications  
Several approaches were implemented using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) longitudinal study.  ...  The results demonstrate the utility of machine learning in the prediction of cognitive impairment based on modifiable risk factors and may encourage interventions to reduce the prevalence or severity of  ...  Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (  ... 
doi:10.14569/ijacsa.2019.0100820 fatcat:4uxrffbw7zcclm6x6l2mhg4sum

Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell

Manuel Milling, Florian B. Pokorny, Katrin D. Bartl-Pokorny, Björn W. Schuller
2022 Frontiers in Digital Health  
Moreover, we give an excerpt of recent studies on the automatic audio-based detection of diseases ranging from acute and chronic respiratory diseases via psychiatric disorders to developmental disorders  ...  Finally, we contextualize and outline application scenarios of speech-based disease detection systems as supportive tools for health-care professionals under ethical consideration of privacy protection  ...  A popular approach in this regard is the use of bag-of-audio-words (BoAW) representations to summarize signal characteristics over time by means of their frequency (19) .  ... 
doi:10.3389/fdgth.2022.886615 pmid:35651538 pmcid:PMC9149088 fatcat:klw5zr6vxrgo5cqmdwbgp3tjnm

Imaging of brain activity and behavioral disorders

E H Rubin
1986 Psychiatric developments  
In evaluating the results of imaging studies, it is important to establish whether the reported changes correlate with episodic symptomatology or with a chronic disease process.  ...  Affective disorders exemplify conditions which are episodic, progress rapidly and respond to medication, making it difficult to identify consistent patterns of metabolic change.  ...  RUBIN bag y ate  ... 
pmid:3010277 fatcat:qoks33x6mffnhoa3ev3g4fpgl4
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