Machine learning technique for early detection of Alzheimer’s disease

Kumari, Rashmi; Nigam, Akriti; Pushkar, Shashank

doi:10.1007/s00542-020-04888-5

Cited by 24 publications

(19 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work improved the sensitivity by 16.5%; • Specificity: The specificity was reported only in our work and [20]. Our work improved the specificity by 7.11%; • Accuracy: our work improved the accuracy by 7.97-31.0% compared with [19][20][21][22].…”

Section: Oasis-3mentioning

confidence: 63%

“…• Class and sample size: Our work and [20,21] utilized the full set of OASIS-3 for fourclass AD detection. Work [19] formulated a binary AD detection model, whereas work [20] designed a one-class AD detection model; • Features and algorithms: One work [21] separated the feature extraction and AD detection into two parts using two algorithms. Our work and [19,20,22] formulated the feature extraction and AD detection with one algorithm; • Type of cross-validation: The existing works [19][20][21][22] did not employ cross-validation.…”

Section: Oasis-3mentioning

confidence: 99%

“…Work [19] formulated a binary AD detection model, whereas work [20] designed a one-class AD detection model; • Features and algorithms: One work [21] separated the feature extraction and AD detection into two parts using two algorithms. Our work and [19,20,22] formulated the feature extraction and AD detection with one algorithm; • Type of cross-validation: The existing works [19][20][21][22] did not employ cross-validation. Fivefold cross-validation was adopted in our work; • Sensitivity: The sensitivity was reported only in our work and [20].…”

Section: Oasis-3mentioning

confidence: 99%

“…Only a portion of the dataset was considered in the model implementation and performance analysis in works [13,14,16,18,19]; • Some of the existing works did not employ cross-validation [13,[15][16][17][18][19][20][21][22] and improperly defined the ratio of cross-validation [11] in the performance evaluation and analysis of the AD detection models. The trained models without cross-validation may not be designed with optimal sets of hyperparameters and may be more prone to model overfitting;…”

mentioning

confidence: 99%

See 3 more Smart Citations

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Chui¹,

Gupta

Alhalabi

et al. 2022

Diagnostics

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85–3.88%, 2.43–2.66%, and 1.8–40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively.

show abstract

Section: Oasis-3mentioning

confidence: 63%

Section: Oasis-3mentioning

confidence: 99%

Section: Oasis-3mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Chui¹,

Gupta

Alhalabi

et al. 2022

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Early diagnosis and analysis of medical images and the discovery and creation of new treatments can be assisted by machine learning through the application of several high-dimensional data sources [5]. An efficient machine learning model successfully diagnosed self-detected Alzheimer's disease and mild cognitive impairment converter or non-converter during pre-stages [6]. Support vector machine (SVM), artificial neural network (ANN), and deep learning are the most extensively used classification approaches.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

Longitudinal Neuroimaging Data Classification for Early Detection of Alzheimer’s Disease using Ensemble Learning Models

Tripathi¹,

Augustin²,

Kim³

2022

Preprint

View full text Add to dashboard Cite

<div> <div> <div> <p>This paper applies Ensemble Learning models for the early detection of Alzheimer’s disease in elderly adults. The publicly available dataset from the Open Access Series of Imaging Studies (OASIS) Database is used. A novel longitudinal MRI data-based machine learning model is proposed in the paper, which takes account of features like- Mini-Mental State Examination (MMSE) score and years of education to make a generalized classifier. Our proposed model achieved a 5-fold cross-validation area under the curve (AUC) score of 89.93% and accuracy of 94.64%. We show that our results quantitatively outperform the state-of-the-art in Alzheimer’s disease detection. We then compared our results to other previous state-of-the-art research and our model’s metrics surpasses them. </p> </div> </div> </div>

show abstract

Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models

Singh

Mishra

2023

Springer Proceedings in Mathematics &Amp; Statistics

View full text Add to dashboard Cite

Machine learning technique for early detection of Alzheimer’s disease

Cited by 24 publications

References 17 publications

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Longitudinal Neuroimaging Data Classification for Early Detection of Alzheimer’s Disease using Ensemble Learning Models

Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models

Contact Info

Product

Resources

About