Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images

Ge, Chenjie; Qu, Qixun; Gu, Irene Yu‐Hua; Jakola, Asgeir Store

doi:10.1016/j.neucom.2019.04.023

Cited by 54 publications

(15 citation statements)

References 10 publications

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“…According to the data in Table 6, which represents a comparison between our proposed research and the previous research, our research considered as the first research that utilized cerebral blood flow biomarker from DSA scans for Alzheimer's disease detection. Moreover, it has outperformed the previous works [52][53][54][55][56][57]60,61,[63][64][65][66][67] in terms of accuracy with a 96.7% score. To guarantee a fair comparison, the researchers in [61,65,66] have utilized 3D an 4D scans in their methodologies, while our proposed methodology utilized 2D scans of distinct modality.…”

Section: Comparison With Previous Workmentioning

confidence: 76%

“…Moreover, it has outperformed the previous works [52][53][54][55][56][57]60,61,[63][64][65][66][67] in terms of accuracy with a 96.7% score. To guarantee a fair comparison, the researchers in [61,65,66] have utilized 3D an 4D scans in their methodologies, while our proposed methodology utilized 2D scans of distinct modality. Additionally, the researchers in [63] utilized two databases and train their model on about thousand samples, while our proposed model has trained on few numbers of samples, and it has obtained efficient results.…”

Section: Comparison With Previous Workmentioning

confidence: 76%

“…Ge et al [61] used 3D MRI scans for 198 patients with Alzheimer's disease and 139 normal subjects from the ADNI database. A 3D-multiscale-CNN was constructed to extract the multiresolution features, and parallel 3D multiscale-CNNs were performed on three regions of the tissue image, which are cerebrospinal fluid, white matter, and grey matter.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh

Almahmoud

Ali

et al. 2021

BDCC

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Neuroimaging refers to the techniques that provide efficient information about the neural structure of the human brain, which is utilized for diagnosis, treatment, and scientific research. The problem of classifying neuroimages is one of the most important steps that are needed by medical staff to diagnose their patients early by investigating the indicators of different neuroimaging types. Early diagnosis of Alzheimer’s disease is of great importance in preventing the deterioration of the patient’s situation. In this research, a novel approach was devised based on a digital subtracted angiogram scan that provides sufficient features of a new biomarker cerebral blood flow. The used dataset was acquired from the database of K.A.U.H hospital and contains digital subtracted angiograms of participants who were diagnosed with Alzheimer’s disease, besides samples of normal controls. Since each scan included multiple frames for the left and right ICA’s, pre-processing steps were applied to make the dataset prepared for the next stages of feature extraction and classification. The multiple frames of scans transformed from real space into DCT space and averaged to remove noises. Then, the averaged image was transformed back to the real space, and both sides filtered with Meijering and concatenated in a single image. The proposed model extracts the features using different pre-trained models: InceptionV3 and DenseNet201. Then, the PCA method was utilized to select the features with 0.99 explained variance ratio, where the combination of selected features from both pre-trained models is fed into machine learning classifiers. Overall, the obtained experimental results are at least as good as other state-of-the-art approaches in the literature and more efficient according to the recent medical standards with a 99.14% level of accuracy, considering the difference in dataset samples and the used cerebral blood flow biomarker.

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Section: Comparison With Previous Workmentioning

confidence: 76%

Section: Comparison With Previous Workmentioning

confidence: 76%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Gharaibeh

Almahmoud

Ali

et al. 2021

BDCC

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“…Similarly, Zhu et al ( 10 ) only computed the volume of GM as a feature for each region of the 93 regions of interest in the labeled MR image and used multiple-kernel learning to classify the neuroimaging data. These studies indicate that GM tissue is the most important area for AD classification using MRI ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis

Song

Jian

et al. 2021

Front. Digit. Health

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Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called “GM-PET.” The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.

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“…The Alzheimer Disease is an irremediable, Neurodegenerative and dynamic brain diseases that directly affect the individual's memory, learning, thoughts and behaviour [1]. Currently, AD is a third leading cause of death after cancer and heart disease in the world.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer Disease Detection Based on Deep Neural Network with Rectified Adam Optimization Technique using MRI Analysis

Suresha

Parthasarathy²

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Alzheimer is a memory depletion disease, which is widely recognized as dementia. The research on early detection of dementia has received huge interest among the researchers to help in reducing mortality rates of Alzheimer's patients. In recent years in the medical field, the deep learning techniques play an important role in computer aided diagnosis. In this research, the automatic recognition of Alzheimer Disease (AD) based on the Magnetic Resonance Imaging (MRI) is accomplished by implementing an unsupervised classification technique named Deep Neural Network (DNN) with the rectified Adam optimizer. At first, Histogram of Oriented Gradients (HOG) is utilized to extract the feature values from the brain images, which were acquired from National Institute of Mental Health and Neurosciences (NIMHANS) and Alzheimer disease Neuroimaging Initiative (ADNI) datasets. Next, the extracted features were given as the input to DNN with the rectified Adam optimizer to distinguish the healthy, AD and Mild Cognitive Impairment (MCI) patients. The experimental results have revealed that the HOG-DNN with the rectified Adam optimizer has achieved better performance in AD recognition and showed 16% enhancement in classification accuracy compared to other existing work; Landmark based features with support vector machine classifier.

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Multi-stream multi-scale deep convolutional networks for Alzheimer’s disease detection using MR images

Cited by 54 publications

References 10 publications

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches

An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis

Alzheimer Disease Detection Based on Deep Neural Network with Rectified Adam Optimization Technique using MRI Analysis

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