An Overview of the Study on CNN Techniques for Early Alzheimer’s disease Diagnosis
Keywords:
Alzheimer’s disease (AD), Artificial intelligence, Convolutional neural network, EEG, Machine learningAbstract
This study offers a thorough analysis of Alzheimer’s disease (AD), emphasising two machine learning (ML) techniques for early identification. Early indications of AD, a degenerative neurocognitive condition, may include linguistic difficulties, behavioural abnormalities, and memory loss. For the development of successful treatments, early diagnosis is essential. A branch of artificial intelligence (AI), ML enables systems to examine and learn from large, complex datasets using probabilistic and optimisation techniques. The review discusses important topics such as dataset selection, evaluation criteria, and classification techniques while highlighting current studies that used the ADNI dataset. The study specifically looks at two models: a 3D CNN and an 18-layer CNN. By successfully separating data into training and testing subsets, the 18-layer CNN achieves an accuracy of 98%, outperforming the 3D CNN, according to the results. The potential of deep learning, particularly CNNs, to improve early-stage AD diagnosis is highlighted by this study.
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