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Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world and the most common cause of dementia. In recent times, accurate and early detection of AD plays a vital role in patient care and further treatment. Lately, studies on AD diagnosis has attached a great significanceto artificial-based diagnostic algorithms. During this master thesis we explore
how deep learning models can handle neuroimages in order to identify andpredict the evolution of the disease. Different from the traditional machine learning algorithms, deep learning does not require manually extracted features but instead utilizes 3D image processing models to learn features for the diagnosis and the prognosis of AD. The contribution of this work relies on a
more rigorous preprocessing phase involving skull-stripping and intensity normalization of the medical images. The hippocampus, a brain area critical for learning and memory, is especially affected at early stages of Alzheimer’s disease. In some parts of this work, It will be used as a region of interest for our algorithms that will consist in convolutional neural networks, the typical image classifier models, and vision transformers, a novel deep learning architecture.

Deep learning --- Machine learning --- Alzheimer's disease --- convolutional neural network (CNN) --- Vision transformer (ViT) --- classification --- region of interest --- ADNI --- Ingénierie, informatique & technologie > Sciences informatiques