A Deep Learning Method on Medical Image Dataset Predicting Early Dementia in Patients Alzheimer’s Disease using Convolution Neural Network (CNN)
N. Deepa1, S P. Chokkalingam2
1N. Deepa, Assistant Professor, Department of Computer Science and Engineering, Saveetha Institute of Engineering Medical and Technical Sciences, Chennai (Tamil Nadu), India.
2S P. Chokkalingam, Professor, Department of Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
Manuscript received on 03 October 2019 | Revised Manuscript received on 12 October 2019 | Manuscript Published on 22 October 2019 | PP: 604-609 | Volume-8 Issue-3S October 2019 | Retrieval Number: C11211083S19/2019©BEIESP | DOI: 10.35940/ijrte.C1121.1083S19
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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: Memory loss is one of the major dementia where the human has a common loss of memory which shows the person to behave worst and they don’t care them properly. Alzheimer’s disease (Ad) is a neurodegenerative disease which affects the brain with mild cognitive impairment. As MCI has several phases where treatment can be consider for avoiding side effects. Deep Learning techniques is the current trend which can handle the images, massive datasets such as unsupervised, supervised and reinforcement progress. A human MRI images is deal with the existing system to find the dementia. In Existing system 82.51% accuracy of classification of neural network was identified . Due to several limitations of existing system CNN was proposed. To predict the dementia an algorithm named Logistic regression is used to produce the accuracy more than a loss function. To the test accuracy betterment OASIS project dataset is utilized.
Keywords: MCI, CNN, Alzheimer’s Disease, MRI, Logistic Regression, Deep Learning.
Scope of the Article: Deep Learning