Abstract
Alzheimer’s disease is a kind of dementia which leads in progressive loss of memory usually in elderly persons. Since there is not any cure for this condition it is vital to discover it as soon as possible. Machine learning algorithms are being used to detect various stages of Alzheimer’s disease. ADNI, the most comprehensive dataset has been collected and used to conduct the experiments. The dataset comprises three classifications that include Alzheimer's Disease, Mild Cognitive Impairment and Cognitive Normal. The proposed approach illustrates multiple preprocessing methods to transform 3-D images into 2D images and employs various CNN models to achieve the best performing ones. Preprocessing approaches include brain segmentation, conversion to MNI space etc. VGG19 model has the overall best performance among all other models with an accuracy of 94.25% outperforming many other similar works.
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Acknowledgement
The dataset was obtained from Alzheimer’s Disease Neuroimaging Initiative also known as ADNI. Access to the ADNI database was granted upon request. We would like to thank ADNI for providing us with the necessary data for this research.
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Tahzib-E-Alindo, Kubi, P., Islam, A., Zaher, M.A.H.B., Ripon, S.H. (2023). A Deep Learning-Based Technique to Determine Various Stages of Alzheimer’s Disease from 3D Brain MRI Images. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_18
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