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Segmentation of Multimodal MRI of Hippocampus Using 3D Grey-Level Morphology Combined with Artificial Neural Networks [chapter]

Roger Hult, Ingrid Agartz
2005 Lecture Notes in Computer Science  
This paper presents an algorithm for improving the segmentation from a semi-automatic artificial neural network (ANN) hippocampus segmentation of co-registered T1-weigthted and T2-weighted MRI data, in  ...  Due to the morphological complexity of the hippocampus and the difficulty of separating from adjacent structures, reproducible segmentation using MR imaging is complicated.  ...  For the segmentation of the hippocampus artificial neural nets [11] , [12] (ANN) are being used. The ANN uses a fully connected feed-forward neural net with three layers.  ... 
doi:10.1007/11499145_29 fatcat:ucsl654onbd5hg7trmpjeas3fe

Alzheimer's Detection By Using Neural Networks

Kavita Patil, Geetanjali Kale
2020 Zenodo  
Classification and detection is done using artificial neural network (ANN) which is very reliable and accurate technique. Neural network is trained using number of sample data and extracted feature.  ...  MRI (magnetic resonance imaging) scans of brains are used for detection and classification purpose. First stage is to preprocess the MRI images and then we segment it.  ...  Feature extraction can be done in different ways like using morphological operation, based on region of interest, or using grey level co-occurrence matrix on segmented image.  ... 
doi:10.5281/zenodo.3692367 fatcat:wp3parqpobdi5ml3ciml3kg35y

A Survey on Classification algorithms of Brain Images in Alzheimer's disease based on Feature Extraction techniques

Ruhul Amin Hazarika, Arnab Kumar Maji, Samarendra Nath Sur, Babu Sena Paul, Debdatta Kandar
2021 IEEE Access  
A novel approach for the classification of AD from MRIs by using the fuzzy neural network is proposed in the literature [120] .  ...  The regions, where a significant decrement of grey matter is taken place are segmented using a 3D mask and the "MarsBaR region of interest" toolbox.  ... 
doi:10.1109/access.2021.3072559 fatcat:cc4ffd325naozaxs63geaut76i

Review on Early Detection of Alzheimer's Disease using Neuroimaging Techniques

Vishnu N, R Vaidya, Chaitra N, Srinidhi S P, Shreyas B
2021 Zenodo  
Machine learning, neuroimaging, and deep learning neural networks are few of the techniques which are compared and analysed based on their performance and accuracy.  ...  Each model is critically analysed and provided with limitations, advantages, and best application.  ...  in association with 3D convolutional neural networks.  ... 
doi:10.5281/zenodo.4420080 fatcat:56eec7xvc5hxbgihmqmvz7gsxu

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present  ...  Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review [article]

Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
2021 arXiv   pre-print
We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET.  ...  deep learning approach, such as the convolutional neural networks and autoencoders.  ...  Shallow neural networks Neural network refers to the machine learning models that have a layered network architecture with the layer composed of many artificial neurons.  ... 
arXiv:2102.03336v3 fatcat:mryusowfbjfjldmx46zcwu6dja

Advances in Multimodal Data Fusion in Neuroimaging: Overview, Challenges, and Novel Orientation

Yu-Dong Zhang, Zhengchao Dong, Shui-Hua Wang, Xiang Yu, Xujing Yao, Qinghua Zhou, Hua Hu, Min Li, Carmen Jiménez-Mesa, Javier Ramirez, Francisco J. Martinez, Juan Manuel Gorriz
2020 Information Fusion  
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities.  ...  of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification.  ...  Functional MRI techniques are used to detect the deficits in neural networks of patients with schizophrenia [195] .  ... 
doi:10.1016/j.inffus.2020.07.006 pmid:32834795 pmcid:PMC7366126 fatcat:3cmhcplb5bf2fgpx3kukifbj74

SynthSeg: Domain Randomisation for Segmentation of Brain Scans of any Contrast and Resolution [article]

Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias
2021 arXiv   pre-print
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains.  ...  robustness to a wide range of morphological variability.  ...  Iglesias, “Partial mers, “Automatic anatomical brain MRI segmentation combining Volume Segmentation of Brain MRI Scans of Any Resolution label propagation and decision  ... 
arXiv:2107.09559v2 fatcat:lxppdyslkffg7hxggk3ztcs73m

Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

S Matoug, A Abdel-Dayem, K Passi, W Gross, M Alqarni
2012 Journal of Physics, Conference Series  
We discuss an automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs 2-dimensional (volume slices) and volumetric segmentation methods in order to segment  ...  We assessed the performance of the classifiers by using results from the clinical tests.  ...  The second method is the neural network which is a learning system that creates interconnecting artificial neurons.  ... 
doi:10.1088/1742-6596/341/1/012019 fatcat:niplou5avndppjjswtyeka2qfq

Efficient Machine Learning Techniques to Diagnose and Predict Alzheimer's disease

2020 International Journal of Engineering and Advanced Technology  
Furthermore this article includes major categories of machine learning algorithms that include artificial neural networks, Support vector machines and Deep learning based ensemble models that helps the  ...  Despite of the development of numerous machine learning models for early diagnosis of Alzheimer's disease, it is observed that there is a lot more scope of research.  ...  [52] used a hybrid PSO with the artificial bee colony (ABC) optimization algorithm along with a feed forward neural network (FFNN) in pointing out the problems of prior knowledge in manual ROI selection  ... 
doi:10.35940/ijeat.c6508.029320 fatcat:wzyju5k4wrhgtdcc5h74bqjwve

Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

Ramesh Kumar Lama, Jeonghwan Gwak, Jeong-Seon Park, Sang-Woong Lee
2017 Journal of Healthcare Engineering  
Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations.  ...  Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.  ...  The commonly used classifiers include support vector machine (SVM), artificial neural network (ANN), and other ensemble classifiers.  ... 
doi:10.1155/2017/5485080 pmid:29065619 pmcid:PMC5494120 fatcat:f7mgecqlarfhdddzvywuvumcyy

Regional thinning of the cerebral cortex in schizophrenia: Effects of diagnosis, age and antipsychotic medication

Ragnar Nesvåg, Glenn Lawyer, Katarina Varnäs, Anders M. Fjell, Kristine B. Walhovd, Arnoldo Frigessi, Erik G. Jönsson, Ingrid Agartz
2008 Schizophrenia Research  
Multimodal neuroimaging studies combining structural MRI with DTI, functional MRI, and electrophysiological methods may provide knowledge about the neural circuitry involved.  ...  In BRAINS, the continuous tissue classified image is used as input into the neural network structure identification module.  ... 
doi:10.1016/j.schres.2007.09.015 pmid:17933495 fatcat:sczruifmtjcb3dwywtddpslxum

The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence

Anuschka Silva-Spínola, Inês Baldeiras, Joel P. Arrais, Isabel Santana
2022 Biomedicines  
This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient  ...  In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.  ...  Acknowledgments: The authors want to acknowledge and thank the support given by the multidisciplinary teams that are part of the affiliated institutions.  ... 
doi:10.3390/biomedicines10020315 pmid:35203524 pmcid:PMC8869403 fatcat:axujetc3fbfglezmd4nbkwh2yu

How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database

StavrosI Dimitriadis, Dimitris Liparas, ADNI
2018 Neural Regeneration Research  
Finally, we described our RF-based model that gave us the 1 st position in an international challenge for automated prediction of MCI from MRI data.  ...  Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single  ...  of MCI from MRI data generated an additional 340 artificial test observations that were joined with the real blind test set (4 × 40 = 160) to form a combined test set of 500 observations.  ... 
doi:10.4103/1673-5374.233433 pmid:29926817 pmcid:PMC6022472 fatcat:nepwpibwszf5zcxfkhmf5btx3u

Ensemble Convolutional Neural Networks with Support Vector Machine for Epilepsy Classification Based on Multi-Sequence of Magnetic Resonance Images

Irwan Budi Santoso, Yudhi Adrianto, Anggraini Dwi Sensusiati, Diah Puspito Wulandari, I Ketut Eddy Purnama
2022 IEEE Access  
The combination of predictions uses majority voting, weighted majority voting, and weighted average.  ...  The convolutional neural network (CNN) models on the proposed method are base-learner models with different architectures and have low parameters.  ...  [12] also performed FCD lesion detection by combining quantitative multimodal surface features with an artificial neural network (ANN) to assess its clinical value.  ... 
doi:10.1109/access.2022.3159923 fatcat:tx5hwmxku5bllbam7hgx5f772u
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