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- W3122534477 abstract "Alzheimer's disease is irreparable neurological damage caused to the cognitive functioning of the human body. A previous diagnosis of Alzheimer's will help to deal with the cure of this disease. Studies supporting the ailment have applied many mathematical and machine learning models. Magnetic resonance imaging is a normal procedure involved in the clinical diagnosis of the disease. However, there are a few challenges to its diagnosis due to variations in its MRI samples and their stability concerning the healthy people. Presently, to assess fundamental brain shifts in magneto resonance imaging (MRI), deep learning methods have been used. Because of its excellent performance in automatic features processing a convolution neural network has become popular with a variety of multilayer perceptrons. Moreover, Ensemble Learning (EL) proved its advantages by integrating several models into the learning system's robustness. Here, we propose a collaborative technique concerning Ensemble learning designed for classifying healthy or Alzheimer's disease people with the help of MR images. We carried out detailed experiments to demonstrate our approach to the open-access sequence of image datasets that outperformed a comparative approach." @default.
- W3122534477 created "2021-02-01" @default.
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- W3122534477 date "2020-11-06" @default.
- W3122534477 modified "2023-09-27" @default.
- W3122534477 title "Neurological disease prediction using ensembled Machine Learning Model" @default.
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- W3122534477 doi "https://doi.org/10.1109/pdgc50313.2020.9315758" @default.
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