Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm

Pruthviraja, Dayananda; Nagaraju, Sowmyarani C.; Mudligiriyappa, Niranjanamurthy; Raisinghani, Mahesh S.; Khan, Surbhi Bhatia; Alkhaldi, Nora A.; Malibari, Areej A.

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.

Citation

Pruthviraja, D., Nagaraju, S. C., Mudligiriyappa, N., Raisinghani, M. S., Khan, S. B., Alkhaldi, N. A., & Malibari, A. A. (in press). Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm. Diagnostics, 13(16), 2687. https://doi.org/10.3390/diagnostics13162687

• Journal Article Type: Article
• Acceptance Date: Jul 28, 2023
• Online Publication Date: Aug 15, 2023
• Deposit Date: Sep 19, 2023
• Publicly Available Date: Sep 19, 2023
• Journal: Diagnostics
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 16
• Pages: 2687
• DOI: https://doi.org/10.3390/diagnostics13162687
• Keywords: Clinical Biochemistry