Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Folego, Guilherme; Weiler, Marina; Casseb, Raphael Fernandes; Pires, Ramon; Rocha, Armando Freitas da

doi:10.3389/fbioe.2020.534592

Cited by 73 publications

(35 citation statements)

References 45 publications

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“…In recent years, there is a large amount of research on the classification and prediction of early AD using traditional machine learning methods [16], and the classification and prediction of early AD using deep learning technology is a common occurrence [17]. For example, Folego et al (2020) [18] used CNNs to process MRI images, and the classification accuracy of AD and HC reached 0.97. Perezn et al (2019) [19] connected multiple image blocks to classify the MRI samples in the ADNI database, and the accuracy rate can reach 0.95. erefore, to construct a simple, stable, and accurate early diagnosis system for AD is important for early intervention and treatment of AD.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

Liu

et al. 2021

Journal of Healthcare Engineering

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The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

Liu

et al. 2021

Journal of Healthcare Engineering

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“…Deep learning networks have to be trained on massive datasets to achieve good performance [18]. Therefore, when the original dataset contains a limited number of images, data augmentation [19] is required to improve accuracy and prevent overfitting [20].…”

Section: Image Preprocessing and Augmentationmentioning

confidence: 99%

BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

et al. 2021

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Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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“…This highlights a lack of methodological transparency across the considered research, especially considering the many different subjective choices required during model construction that can have misleading effects on the overall performance of the system. Of the studies that did make code available, no paper provided detailed tutorials of preprocessing and model construction -understandably, the quality and thoroughness of reported code is another important aspect of reproducibility and transparency which is not solved by making code available [55,57,58,59,60,48]. Additionally, most papers considered were from journals, meaning that the majority of studies underwent some form of peer review (43/55)…”

Section: Transparencymentioning

confidence: 99%

Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks

O’Connell

Cannon

Broin

2021

Preprint

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Brain disorders are characterised by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration, and comprise several psychiatric and neurological disorders. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Given that symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation, structural magnetic resonance imaging (MRI) data of the brain are an important focus of research studies, particularly for predictive modelling. With the advent of large MRI data consortiums (such as the Alzheimer’s Disease Neuroimaging Initiative) facilitating a greater number of MRI-based classification studies, convolutional neural networks (CNNs), which are multi-layer representation-based models particularly well suited to image processing, have become increasingly popular for research into brain conditions. Despite this, modelling practices, the degree of transparency, and considerations of interpretability vary widely across studies, making them difficult to both compare and/or reproduce. Modelling practices here refers to issues surrounding the data splitting procedure, the presence or absence of repeat experiments, the critical appraisal of performance metrics, and the overall reliability of the modelling approach. Transparency refers to how detailed the authors’ methodological descriptions are, and the availability of code. Finally, interpretability refers to the attempt made by the authors to identify structural brain alterations driving model predictions – this is particularly important as the application of deep learning systems becomes more widespread in clinical settings. Here, we conduct a systematic literature review of 55 studies carrying out CNN-based predictive modelling of brain disorders using MRI data and critique their modelling practices, transparency, and considerations of interpretability; we furthermore propose several practical recommendations aimed at promoting comprehensive, clear, and reproducible research into brain disorders using MRI-based deep learning models.

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Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Cited by 73 publications

References 45 publications

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease

BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks

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