Autor: |
Pruthviraja D; Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India., Nagaraju SC; Department of Computer Science and Engineering, R V College of Engineering, Bengaluru 560059, India., Mudligiriyappa N; Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Bengaluru 560064, India., Raisinghani MS; College of Business, Texas Woman's University, Denton, TX 76204, USA., Khan SB; Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester M54WT, UK., Alkhaldi NA; Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia., Malibari AA; Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia. |
Abstrakt: |
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. |