Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

Islam, Jyoti; Zhang, Yanqing

doi:10.1186/s40708-018-0080-3

Cited by 325 publications

(163 citation statements)

References 74 publications

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“…Convolution neutral network, a powerful image recognition tool based on deep learning, was used for Alzheimer's disease diagnoses because MRI images were the features. 29 An accuracy of 93.18% was achieved by their proposed model and showed the significance of machinelearning for an Alzheimer's disease diagnosis. For research on identifying chemo-brains, the study cited in the introduction of this paper 5 used DMN connectivity from rs-fMRI as features and SVM classifier for discriminating chemotherapy-treated (C + ) BC survivors from non-chemotherapytreated (C -) BC survivors and HCs.…”

Section: Discussionmentioning

confidence: 87%

“…Deep‐learning, a specialized branch of machine‐learning, is also a formidable model for classifying images into several groups. Convolution neutral network, a powerful image recognition tool based on deep learning, was used for Alzheimer's disease diagnoses because MRI images were the features . An accuracy of 93.18% was achieved by their proposed model and showed the significance of machine‐learning for an Alzheimer's disease diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Chen

Lin

Yeh

et al. 2018

Magnetic Resonance in Med

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Purpose Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo‐brain. In this study, we aimed to construct machine‐learning models to detect the subtle alternations of the brain in postchemotherapy BC patients. Methods Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting‐state functional MRI and generalized q‐sampling imaging (GQI). Results Logistic regression (LR) with GQI indices in standardized voxel‐wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel‐wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo‐brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave‐one‐out cross‐validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets. Conclusions In our study, we constructed the machine‐learning models that were able to identify chemo‐brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo‐brains in the future.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Chen

Lin

Yeh

et al. 2018

Magnetic Resonance in Med

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“…This means that the model remembers the details of the training set and it does not generalize based on the validation or testing sets. Data augmentation is usually used to avoid the effect of overfitting [35]. It is a process which artificially creates new data from the existing training set using class-preserving perturbations of a dataset, which accordingly prevent overfitting which might happen when a fully connected layer inhabits most of the parameters.…”

Section: Data Augmentationmentioning

confidence: 99%

Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

2020

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The increasing rates of neurodevelopmental disorders (NDs) are threatening pregnant women, parents, and clinicians caring for healthy infants and children. NDs can initially start through embryonic development due to several reasons. Up to three in 1000 pregnant women have embryos with brain defects; hence, the primitive detection of embryonic neurodevelopmental disorders (ENDs) is necessary. Related work done for embryonic ND classification is very limited and is based on conventional machine learning (ML) methods for feature extraction and classification processes. Feature extraction of these methods is handcrafted and has several drawbacks. Deep learning methods have the ability to deduce an optimum demonstration from the raw images without image enhancement, segmentation, and feature extraction processes, leading to an effective classification process. This article proposes a new framework based on deep learning methods for the detection of END. To the best of our knowledge, this is the first study that uses deep learning techniques for detecting END. The framework consists of four stages which are transfer learning, deep feature extraction, feature reduction, and classification. The framework depends on feature fusion. The results showed that the proposed framework was capable of identifying END from embryonic MRI images of various gestational ages. To verify the efficiency of the proposed framework, the results were compared with related work that used embryonic images. The performance of the proposed framework was competitive. This means that the proposed framework can be successively used for detecting END.

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“…An efficient deep convolutional neural network technique by [15] using brain MRI data analysis for diagnosis of AD and the main focus of this research work is on structured magnetic resonance image (sMRI). The model offers a significant improvement for multi-class classification, while most of existing proposed research works accomplish binary classification.…”

Section: F Deep Autoencoder (Da)mentioning

confidence: 99%

Alzheimer’s Disease Diagnosis using Deep Learning Techniques

Salehi¹,

Baglat²,

Gupta³

2020

IJEAT

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Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer's disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.

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Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks

Cited by 325 publications

References 74 publications

Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Predicting chemo‐brain in breast cancer survivors using multiple MRI features and machine‐learning

Deep Learning Techniques for Automatic Detection of Embryonic Neurodevelopmental Disorders

Alzheimer’s Disease Diagnosis using Deep Learning Techniques

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