Abstract
Alzheimer’s disease is a neurodegenerative disease that affects the old age population and is affected by the neurofibrillary tangles and neurotic plagues as they impair the neuron’s microtubule transport system. The onset of this disease leads to a decline in the normal cognitive functioning of a person. The commonly observed symptom of AD is the difficulty in remembering the latest events. Moreover, as the progression occurs in a person, it can include symptoms like issues with the language, mood swings, and behavioral issues. As for the particular disease, no cure has been found out yet, to completely eradicate the disease from the body, therefore detection in advance of the disease has proven to be effective in improving a person's life. In the study, 1.5 T T1 weighted MRI scans were acquired from the Alzheimer’s disease neuroimaging initiative (ADNI) database of 910 patients, where 336 were healthy control, 307 were mild cognitive impairment(MCI), and 267 for Alzheimer’s disease. The study leverages a 3D convolutional neural network (3D-CNN) for learning the intricate patterns in the magnetic resonance imaging (MRI) scans for the detection of Alzheimer’s disease. The 3D-CNN model performed superiorly by plotting an accuracy of 95.88%, precision of 0.951, recall of 0.9601, and f1-score of 0.9538.
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Chakraborty, S., Sain, M., Park, J., Aich, S. (2021). Early Detection of Alzheimer’s Disease from 1.5 T MRI Scans Using 3D Convolutional Neural Network. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A., Kumar, P. (eds) Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-15-7990-5_2
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DOI: https://doi.org/10.1007/978-981-15-7990-5_2
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