Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+ $$\epsilon $$ Study on ADNI

Aderghal, Karim; Boissenin, M.; Benois-Pineau, Jenny; Catheline, Gwénaëlle; Afdel, Karim

doi:10.1007/978-3-319-51811-4_56

Cited by 43 publications

(42 citation statements)

References 12 publications

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“…The first one is the data that were selected from the ADNI-1 screening baseline with only anatomical MRI T1-weighted sequences, in this set all subjects underwent whole-brain MRI scanning on 1.5 Tesla at 14 acquisition sites. It is the same dataset as used in [16] . With the same demographic information for each of the diagnosis groups (NC, AD and MCI), the data sample consists of 815 structural MRIs including 188 Alzheimer's Disease (AD) patients, 228 cognitively normal (NC) and 399 subjects with Mild Cognitive Impairment (MCI).…”

Section: Methodology and Approachmentioning

confidence: 99%

“…This paper is an extension of previous works [16] , [27] , [28] where we proposed a method that combines sMRI and DTI-MD imaging modalities focusing only on the hippocampal region. Moreover, we presented a fusion framework based on the concept of using multiple sources, including cross transfer learning approach.…”

Section: Introductionmentioning

confidence: 92%

“…This happens because the depth of the dataset is not representative of the variability of brain structures, and consequently the classification results on train set are better while on the test set are not, whereas they should be the same when the over-fitting is absent. Specific data augmentation and regularization methods such as Dropout can solve the problem of insufficiency of samples [15] , [16] . An alternative approach called “Transfer Learning” can battle the overfitting phenomena, which consists in reusing the weights of a trained model implemented for different application domain.…”

Section: Introductionmentioning

confidence: 99%

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Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Aderghal

Afdel

Benois-Pineau

et al. 2020

Heliyon

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Background Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images. New Method In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types. Results Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC. Comparison with Existing Method(s) Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning. Conclusions Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

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Section: Methodology and Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Aderghal

Afdel

Benois-Pineau

et al. 2020

Heliyon

Self Cite

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“…To overcome the small sample size limitation of medical images, image augmentation techniques were used (Aderghal et al, 2017). The first technique we applied was Gaussian filters to blur the image to mimic the possible variations in the original images.…”

Section: Methodsmentioning

confidence: 99%

Deep Feature Selection and Causal Analysis of Alzheimer’s Disease

Liu

et al. 2019

Front. Neurosci.

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Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer’s disease (AD). However, it is also well known that DCNNs are “black boxes” owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the development of AD and causal paths from genetic variants to AD via image mediation. The proposed algorithm is applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with diffusion tensor imaging (DTI) in 151 subjects (51 AD and 100 non-AD) who were measured at four time points of baseline, 6 months, 12 months, and 24 months. The algorithm identified brain regions underlying AD consisting of the temporal lobes (including the hippocampus) and the ventricular system.

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“…Recently, deep learning approaches were introduced in classification of brain disease [48], [49], which contain deep belief network [21], [36]- [38], Auto Encoder networks [19], [20], [39], [40], Convolution Neural Networks(CNN) [41]- [47]. Because of the powerful learning ability and automatic features extraction ability of deep learning, these approaches are hopeful to improve classification performance of brain disease.…”

Section: Related Workmentioning

confidence: 99%

Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

et al. 2019

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Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification. INDEX TERMS Schizophrenia classification, multiple features extraction, deep capsule network, classifier ensemble.

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Classification of sMRI for AD Diagnosis with Convolutional Neuronal Networks: A Pilot 2-D+ $$\epsilon $$ Study on ADNI

Cited by 43 publications

References 12 publications

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

Deep Feature Selection and Causal Analysis of Alzheimer’s Disease

Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

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