{Reference Type}: Journal Article
{Title}: Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm.
{Author}: Pruthviraja D;Nagaraju SC;Mudligiriyappa N;Raisinghani MS;Khan SB;Alkhaldi NA;Malibari AA;
{Journal}: Diagnostics (Basel)
{Volume}: 13
{Issue}: 16
{Year}: 2023 Aug 15
{Factor}: 3.992
{DOI}: 10.3390/diagnostics13162687
{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.