Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks

Sarraf, Saman; Tofighi, Ghassem

doi:10.48550/arxiv.1607.06583

Cited by 22 publications

(24 citation statements)

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“…It should be mentioned, that the direct comparison of our method with the reviewed algorithms for Alzheimer's Disease diagnostics is impossible as the results were obtained using images from several databases and with datasets of different size (see Table 1). Moreover, various classification problems were challenged: although most papers focus on the 3-class AD/MCI/NC classification, some of them consider only 2-class AD/NC classification [4,37,39,38] and even 4class AD/eMCI/lMCI/NC classification [44]. Also [6,45] deserve special attention as the authors try to solve a problem of Alzheimer's converters prediction.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, compared with feature-based methods with fusion of the same sMRI and DTI modalities in [10] the CNN classifiers confirm their better performance. As far as full-brain approaches are concerned, such as [37], [47], the consensus cannot be obtained, as the best performances are shown for the work of Payan and Montana with quite a large dataset on a single sMRI modality [5]. Her we should also notice that full-brain schemes require much stronger computational resources as the full resolution 3D scans have to be submitted to the network architecture at once.…”

Section: Discussionmentioning

confidence: 99%

“…In [37], [38], [39] the authors compared the classification of structural and functional MRI images using one of the lightest Deep architectures, the LeNet-5 architecture. They transformed the source 3D and 4D (in the case of fMRI) data to a batch of 2D images.…”

Section: D Convolutional Neural Networkmentioning

confidence: 99%

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3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

Khvostikov¹,

Aderghal²,

Krylov³

et al. 2018

Preprint

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In the last decade, computer-aided early diagnostics of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research.In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the conventional AlexNet-based network using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance of the proposed 3D Inception-based CNN.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

Khvostikov¹,

Aderghal²,

Krylov³

et al. 2018

Preprint

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“…In this work, we use two deep architectures: VGG [13] and ResNet [7], that showed state-of-the-art performance in ImageNet classification challenge in 2014 and 2015 correspondingly. We generalize these architectures to the three-dimensional input size of MR images in the same way as was proposed in [12].…”

Section: Neuroimaging Datamentioning

confidence: 99%

“…It includes Computer Tomography (CT), structural and functional Magnetic Resonance Imaging (sMRI and fMRI respectively) and etc. With a growth of deep learning applications in data analysis, neuroimaging is extensively used in many medical tasks such as image segmentation [1], diagnosis classification [11] and prediction of disease progression [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

Shmulev

Belyaev

2018

Lecture Notes in Computer Science

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Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimers Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem as approximately 15% of patients with MCI converges to AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is ACC = 0.76±0.01 and the area under the ROC curve -ROC AUC = 0.86 ± 0.01.

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Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images

Lanjewar

Parab

Shaikh

2022

Multimed Tools Appl

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Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks

Cited by 22 publications

References 0 publications

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images

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