Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm
Dayananda Pruthviraja,
Sowmyarani C. Nagaraju,
Niranjanamurthy Mudligiriyappa,
Mahesh S. Raisinghani,
Surbhi Bhatia Khan,
Nora A. Alkhaldi,
Areej A. Malibari
Affiliations
Dayananda Pruthviraja
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India
Sowmyarani C. Nagaraju
Department of Computer Science and Engineering, R V College of Engineering, Bengaluru 560059, India
Niranjanamurthy Mudligiriyappa
Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Bengaluru 560064, India
Mahesh S. Raisinghani
College of Business, Texas Woman’s University, Denton, TX 76204, USA
Surbhi Bhatia Khan
Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M54WT, UK
Nora A. Alkhaldi
Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
Areej A. Malibari
Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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.
