Ensemble-of-classifiers-based approach for early Alzheimer’s Disease detection

Rajasree, R.; Rajakumari, S. Brintha

doi:10.1007/s11042-023-16023-3

Cited by 4 publications

(2 citation statements)

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“…To increase the efficacy of the classifier, feature extraction intends to discover the most relevant and valuable set of features (unique properties). The most important step in classifying biomedical signals is feature extraction since improperly chosen features could cause the classification performance to suffer 12 . Unlike traditional methods that are time-consuming and require specialized knowledge for feature extraction, deep learning can automatically extract relevant features from input images, resulting in improved prediction accuracy 23 .…”

Section: Methodsmentioning

confidence: 99%

“…The difficulty of diagnosing AD from MRI scans has been examined, and different approaches have been explored. Modeling the diagnosis as a prediction and classification problem is the most employed approach 9,12,13 . In the light of recent studies Ruiz et al 13 proposed an ensemble of 3D densely connected convolutional network models to perform a 4-way classification of 3D MRI images.…”

Section: Related Workmentioning

confidence: 99%

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Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection

Jenber Belay,

Walle,

Haile

2024

Sci Rep

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Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.

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Section: Methodsmentioning

confidence: 99%