Logistic-Based OVA-CNN Model for Alzheimer’s Disease Detection and Prediction Using MR Images
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 873, Page: 195-206
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Alzheimer’s disease (AD) is a form of dementia that diminishes a patient’s cognition and memory. By predicting AD progression early, physicians can begin medication that may reduce the disease’s progression. AD detection and prediction have been extensively performed using convolutional neural networks (CNNs). Despite this, CNN’s use for AD detection is limited by the number of parameters and the extensive training data required. Consequently, this study proposes a logistically based OVA-CNN model for AD detection and prediction using Magnetic Resonance Images (MRI). OVA-CNN consists of the four binary CNN which classified four classes of AD and the logistic regressor predicts the time conversion of pMCI patient who converts AD. The experimental results on the Alzheimer’s Disease Neuro Imaging Initiative (ADNI) demonstrate that the proposed model achieves a high level of accuracy of 93%.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85195300421&origin=inward; http://dx.doi.org/10.1007/978-981-99-9442-7_18; https://link.springer.com/10.1007/978-981-99-9442-7_18; https://dx.doi.org/10.1007/978-981-99-9442-7_18; https://link.springer.com/chapter/10.1007/978-981-99-9442-7_18
Springer Science and Business Media LLC
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