Alzheimer’s Disease Diagnosis using Deep Learning Techniques
Ahmad Waleed Salehi1, Preety Baglat2, Gaurav Gupta3
1Dr. Gaurav Gupta*, Faculty of Engineering and Technology, Shoolini University, Himachal Pradesh, India.
2Preety Baglat, Faculty of Engineering and Technology, Shoolini University, Himachal Pradesh, India.
3Gaurav Gupta, Faculty of Engineering and Technology, Shoolini University, Himachal Pradesh, India.
Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 874-880 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5345029320/2020©BEIESP | DOI: 10.35940/ijeat.C5345.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer’s disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.
Keywords: Alzheimer’s disease, Deep Learning, CNN, ADNI, Neuroimaging Classification.