Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Lee, Bumshik; Ellahi, Waqas; Choi, Jae Young

doi:10.1587/transinf.2018edp7393

Cited by 37 publications

(11 citation statements)

References 21 publications

(45 reference statements)

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“…In order to investigate the effect of non-central slices for the result, the segmentation experiments were performed using non-central slices. For training, some slices at the side lobes do not contain much useful information [38] for segmentation and a slice shares almost the same information with neighboring slices. Hence, by excluding these non-informative slices and reducing the repetitive training of the consecutive slices, we extracted 48 slices with an interval of 3 slices, which contains both central slices (i.e., slices with more information) and non-central slices (i.e., slice with less information) for training.…”

Section: Plos Onementioning

confidence: 99%

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

2020

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Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-wise U-net architecture for the automatic segmentation of brain structures in structural MRI. In the proposed brain segmentation method, the non-overlapping patch-wise U-net is used to overcome the drawbacks of conventional U-net with more retention of local information. In our proposed method, the slices from an MRI scan are divided into non-overlapping patches that are fed into the U-net model along with their corresponding patches of ground truth so as to train the network. The experimental results show that the proposed patch-wise U-net model achieves a Dice similarity coefficient (DSC) score of 0.93 in average and outperforms the conventional U-net and the SegNetbased methods by 3% and 10%, respectively, for on Open Access Series of Imaging Studies (OASIS) and Internet Brain Segmentation Repository (IBSR) dataset.

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Section: Plos Onementioning

confidence: 99%

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

2020

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“…Table 1 shows the most recent techniques in AD-classification, specifying classification technique, class, dataset, and accuracy of detection. Approaches [2,3,7,8,14,37,43] use traditional Computer vision techniques, while approaches [1,5,19,24,25,29,35,41] use deep CNN for achieving the whole process.…”

Section: Related Workmentioning

confidence: 99%

“…Lee et al [24] proposed a deep CNN data permutation scheme for classification AD using sMRI. They proposed slice selection to achieve the benefits of AlexNet.…”

Section: Related Workmentioning

confidence: 99%

A CNN based framework for classification of Alzheimer’s disease

AbdulAzeem

Bahgat

Badawy

2021

Neural Comput & Applic

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In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer's disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer's disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer's disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer's disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset. Keywords AD-classification Á Convolutional neural network (CNN) Á Magnetic resonance imaging (MRI) Á Adaptive momentum estimation (Adam) Á Glorot uniform weight initializer

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“…This position downplays the importance of expert opinion in model interpretation and preprocessing decisions for medical imaging studies. Without explicit knowledge concerning what image aspects to exclude, researchers can include irrelevant information during model training, as demonstrated by the inclusion of skull and neck information in several studies [60,91,38,84,67,77]. In practice, this would mean the models may have picked up on irrelevant information about neck size or skull thickness, that, if used in clinical applications, could lead to misclassifications of patients with those specific physical characteristics, which may have nothing to do with the condition of interest.…”

Section: Interpretabilitymentioning

confidence: 99%

Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks

O’Connell

Cannon

Broin

2021

Preprint

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Brain disorders are characterised by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration, and comprise several psychiatric and neurological disorders. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Given that symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation, structural magnetic resonance imaging (MRI) data of the brain are an important focus of research studies, particularly for predictive modelling. With the advent of large MRI data consortiums (such as the Alzheimer’s Disease Neuroimaging Initiative) facilitating a greater number of MRI-based classification studies, convolutional neural networks (CNNs), which are multi-layer representation-based models particularly well suited to image processing, have become increasingly popular for research into brain conditions. Despite this, modelling practices, the degree of transparency, and considerations of interpretability vary widely across studies, making them difficult to both compare and/or reproduce. Modelling practices here refers to issues surrounding the data splitting procedure, the presence or absence of repeat experiments, the critical appraisal of performance metrics, and the overall reliability of the modelling approach. Transparency refers to how detailed the authors’ methodological descriptions are, and the availability of code. Finally, interpretability refers to the attempt made by the authors to identify structural brain alterations driving model predictions – this is particularly important as the application of deep learning systems becomes more widespread in clinical settings. Here, we conduct a systematic literature review of 55 studies carrying out CNN-based predictive modelling of brain disorders using MRI data and critique their modelling practices, transparency, and considerations of interpretability; we furthermore propose several practical recommendations aimed at promoting comprehensive, clear, and reproducible research into brain disorders using MRI-based deep learning models.

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Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Cited by 37 publications

References 21 publications

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

A CNN based framework for classification of Alzheimer’s disease

Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks

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