Machine Learning-based Alzheimer’s Disease Prediction using Personalized Methods

Lavanya, M.; Chandan, Radha Raman; Rajasekar, P.; Rham, P. Rethik; Deivakani, M.; Kumar, Ajad

doi:10.1109/icosec54921.2022.9952018

Cited by 7 publications

(1 citation statement)

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“…This allows data collection to continue independent of the influence of a given task on functional brain activity. Alzheimer's disease shrinks the hippocampus and cerebral cortex while expanding the brain's ventricles [13]. The degree of these consequences is influenced by the stage of the disease's development.…”

Section: Introductionmentioning

confidence: 99%

Ad-Tl: Alzheimer’s Disease Prediction Using Transfer Learning

Archana Gopinadhan

2024

jes

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Although many statistical methods and machine learning algorithms have been explored in both clinical and research settings to extract these patterns from neuroimaging data, differentiating between Alzheimer's disease and healthy brain data in older adults (age > 75) has proven challenging due to highly similar patterns of brain atrophy and image intensities. Medical image analysis is just one field that has benefited from the widespread use of deep learning technologies in recent years. This research paper proposed AD prediction using transfer learning (AD-TL) methods. The MRI dataset has been normalized using the Multi-Layer Perception model (MLP) with the CNN algorithm. In order to improve images, the CLAHE (Contrast-Limited Adaptive Histogram Equalization) technique has been used. Image segmentation has been done with Watershed Image segmentation. The training has been done with the Residual network (ResNet 50) with Alex net. Finally, the classification has been done with the Deep Convolutional neural network (DCNN) algorithm.According to the experimental data, the classification accuracy of the technique provided in this study may reach 99%.

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Section: Introductionmentioning

confidence: 99%