Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Qu, Changxing; Zou, Yinxi; Ma, Yingqiao; Chen, Qin; Luo, Jiawei; Fan, Huiyong; Jia, Zhiyun; Gong, Qiyong; Chen, Taolin

doi:10.3389/fnagi.2022.841696

Cited by 11 publications

(6 citation statements)

References 78 publications

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“…To tackle complicated problems [26] and to improve the neural network's efficiency for expression, the activation function is used. We have presented a few incredibly efficient activation functions and their conceptual formulas in Table 2.…”

Section: Categorization Using Cnnmentioning

confidence: 99%

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

SinhaRoy

Sen²

2023

Preprint

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A major research subject in recent times is Alzheimer’s disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of Alzheimer’s disease (AD) is critical for effective treatment and monitoring the time period between normal aging’s unavoidable cognitive loss & dementia’s more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of Alzheimer’s disease (AD) has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNN) to identify Alzheimer’s disease have yielded promising results. Instead of adequately engaging with the lack of Real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer’s disease & utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer’s disease. CNN, DCGANs, and SRGANs are used in this paper to present a Deep Learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data.

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Section: Categorization Using Cnnmentioning

confidence: 99%

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

SinhaRoy

Sen²

2023

Preprint

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“…Nowadays, with the rapid development of artificial intelligence technology, deep learning is widely applied in the detection and treatment of various diseases, such as cancer, diabetes, Alzheimer's disease and Parkinson's disease, and better results have been obtained ( 34 , 35 ). In future research, it is planned to apply deep learning techniques in the prediction of bone metastasis occurring in colorectal cancer.…”

Section: Discussionmentioning

confidence: 99%

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Huang²,

Zhang³

et al. 2022

Front. Public Health

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BackgroundThis study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.MethodsFrom SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models.ResultsFor bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.ConclusionOur study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.

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“…It is important to note that this guide is not extensive and that many other assistive tools exist. For example, there are tools for automating data augmentation and models for data generation (e.g., GANS; Qu et al., 2022 ). Instead, this article only provides an introductory guide to assistive tools for classifying neurological disorders using fMRI and DL.…”

Section: A Guide To Assistive Tools For Fmri and Deep Learning Pipelinesmentioning

confidence: 99%

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

Warren,

Khan,

Moustafa

2024

Brain and Behavior

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BackgroundDeep‐learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain‐specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real‐world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.Objectives and MethodsIn this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.ResultsIn turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.ConclusionsWe are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders.

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Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Cited by 11 publications

References 78 publications

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

WITHDRAWN: A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

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