Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models

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Published: May 4, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i4.6456
Keywords:
Alzheimer’s disease, Deep Learning, VGG16, VGG19, ResNet50

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Ragavamsi Davuluri
Assistant Professor, Department of CSE, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India
Vyshnavi Mallapragada
Student, Department of CSE, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India
Uma Maheswara Rao Mamillapalli
Student, Department of CSE, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India
Manikanta M
Student, Department of CSE, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India
Sireesha Peeka
Student, Department of CSE, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, India

Abstract

Memory loss and impairment are signs of Alzheimer's disease (AD), which may also cause other issues. It has a significant impact on patients' lives and is incurable, but rapid recognition of Alzheimer's disease can be useful to initiate appropriate therapy to avoid further deterioration to the brain. Previously, Machine Learning methodswere used to detect Alzheimer's disease. In recent times, Deep Learning algorithms have become more popular for pattern recognition. This workconcentrates on the recognition of Alzheimer's disease at a preliminary phase using advanced convolutional neural network models. As the disease advances, they steadily forget everything. It is critical to detect the disease as quickly as possible. The proposed model usespre-trained models that uses magnetic resonance imaging of the brain to determine if a person has very mild, mild, moderate, or non-dementia. The models used for classification are VGG16, VGG19, and ResNet50 architectures and provide performance comparison.

Article Details

How to Cite
Davuluri, R. ., Mallapragada, V. ., Mamillapalli, U. M. R. ., M, M. ., & Peeka, S. . (2023). Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 315–323. https://doi.org/10.17762/ijritcc.v11i4.6456
Issue
Vol. 11 No. 4 (2023): April (2023) Issue
Section
Articles

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