scholarly journals Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jong Bin Bae ◽  
Subin Lee ◽  
Wonmo Jung ◽  
Sejin Park ◽  
Weonjin Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Medial Temporal Lobe ◽  
Processing Time ◽  
Resonance Imaging ◽  
Mri Scans

AbstractThe classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.

scholarly journals Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fanar E. K. Al-Khuzaie ◽  
Oguz Bayat ◽  
Adil D. Duru
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Dropout Rate ◽  
Resonance Imaging ◽  
Data Set ◽  
The Brain

There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.

A Magnetic Resonance Imaging Study of White Matter Lesions in Depression and Alzheimer's Disease

1996 ◽  
Vol 168 (4) ◽  
pp. 477-485 ◽  
Author(s):  
John O'brien ◽  
Patricia Desmond ◽  
David Ames ◽  
Isaac Schweitzer ◽  
Susan Harrigan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Magnetic Resonance ◽  
Imaging Study ◽  
White Matter Lesions ◽  
Elderly Subjects ◽  
Resonance Imaging ◽  
Mri Scans

BackgroundWhite matter changes, as revealed by magnetic resonance imaging (MRI), may occur in depression and Alzheimer's disease.MethodT2-weighted MRI scans were performed in 39 control subjects, 61 subjects with NINCDS/ADRDA Alzheimer's disease and 60 subjects with DSM–III–R major depression. Deep white matter lesions (DWML) and periventricular lesions (PVL) were rated on a standard 0–3 scale by two radiologists blind to clinical diagnosis.ResultsAfter controlling for differences in vascular risk factors and current blood pressure, DWML were significantly more common in depressed subjects and PVL in Alzheimer's disease subjects compared to controls. DWML were most common in those presenting in late life with their first ever depression and 50% of such subjects had severe (grade 3) DWML.ConclusionAn association between DWML and depression and PVL and Alzheimer's disease is supported. The increase with DWML that occurs with ageing may predispose some elderly subjects to depression.

scholarly journals Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiao Zhou ◽  
Shangran Qiu ◽  
Prajakta S. Joshi ◽  
Chonghua Xue ◽  
Ronald J. Killiany ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Classification Performance ◽  
Resonance Imaging ◽  
Mri Scans ◽  
The Mean

Abstract Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. Methods T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. Results The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. Conclusion This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.

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