Paper Title :Alzheimer’s Disease Stages Classification Using Mri and Deep Learning
Author :Piyush Nagpal, Jay Raviraj Deore
Article Citation :Piyush Nagpal ,Jay Raviraj Deore ,
(2023 ) " Alzheimer’s Disease Stages Classification Using Mri and Deep Learning " ,
International Journal of Electrical, Electronics and Data Communication (IJEEDC) ,
pp. 42-46,
Volume-11,Issue-5
Abstract : Alzheimer’s disease is a progressive and irreversible brain disorder that affects memory and cognitive abilities.
Early and accu-rate diagnosis of the disease is crucial for effective treatment and man-agement. In this study, we evaluated
the performance of three different deep learning models, namely, CNN, InceptionV3, and EfficientNet B0, for the
classification of Alzheimer’s disease stages using MRI images. We trained and validated these models on a dataset of 5,121
images and evaluated their performance on a test set of 1,279 images. Our results showed that the CNN model achieved the
highest accuracy of 98.75% with a low loss of 5.28%. In comparison, InceptionV3 achieved an ac-curacy of 62.39% and a
loss of 128.90, while EfficientNet B0 achieved an accuracy of 97.03% with a loss of 7.68. These results suggest that transfer
learning using deep learning models can be an effective tool for accurate and early diagnosis of Alzheimer’s disease. Our
study provides insights into the potential of deep learning models for the development of non-invasive and accurate
diagnostic tools for Alzheimer’s disease.
Keywords - Alzheimer’s disease
Type : Research paper
Published : Volume-11,Issue-5
DOIONLINE NO - IJEEDC-IRAJ-DOIONLINE-19761
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Copyright: © Institute of Research and Journals
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Published on 2023-08-24 |
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