Abstract
Dementia is the brain disorder that effects the mental cognitive function. Dementia has different stages, namely C normal cognitive (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Mild cognitive impairment causes turn down in conscious thought and thinking ability. A person with MCI is at an increased risk of developing Alzheimer’s disease. Hence, it is important to determine AD at an earlier stage which is mild cognitive impairment (MCI) as toxic changes may start in the brain at the beginning stage of the Alzheimer’s disease. The proposed work is to detect MCI using MRI images. Firstly, the data samples are subjected to segmentation from which the 3D images of grey matter of each MRI sample are extracted. Then, the resulting images are pre-processed using 3D wavelet transform (DWT) which is fed to a machine learning classifier. The proposed work includes unique and efficient hybrid machine learning algorithm to classify between CN, MCI and AD subjects.
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Acknowledgements
We would like to express our gratitude towards our project guide and other faculties who helped us genuinely with all important guidance and support to complete this project successfully. And we sincerely extend our gratitude towards the institution for providing all the facility required to reach our project to successful work. We would also like to thank our friends for giving various suggestions that was very informative and helpful.
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Sujatha Kumari, B.A., Yadiyala, A.G.V., Aruna, B.J., Radha, C., Shwetha, B. (2021). Early Detection of Mild Cognitive Impairment Using 3D Wavelet Transform. In: Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8530-2_36
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DOI: https://doi.org/10.1007/978-981-15-8530-2_36
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