ArticleDiagnostics (Basel, Switzerland)2023
Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Brain functional connectivity analysis of fMRI-based Alzheimer's disease data.Frontiers in medicine · 2025Article
- Enhancing healthcare recommendation: transfer learning in deep convolutional neural networks for Alzheimer disease detection.Frontiers in medicine · 2024Article
- HDFT-MViT: a progressive core-enhanced mix framework for Alzheimer's disease classification using MRI images.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.