Designing a clinical decision support system for Alzheimer’s diagnosis on OASIS-3 data set

Salami, Farzaneh; Bozorgi-Amiri, Ali; Hassan, Ghulam Mubashar; Tavakkoli–Moghaddam, Reza; Datta, Amitava

doi:10.1016/j.bspc.2022.103527

Cited by 20 publications

(23 citation statements)

References 32 publications

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Section: Oasis-3mentioning

confidence: 99%

“…• 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%

“…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]. Our work improved the sensitivity by 16.5%; • Specificity: The specificity 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%

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

Chui¹,

Gupta

Alhalabi

et al. 2022

Diagnostics

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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.

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