Deep learning-based multitask and multimodal study to assess the progressive nature of Alzheimer's disease

Project Details


Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is irreversible. Theprogressive nature of the disease affects the ability to carry out everyday activities in an early stage and thelater severe stages; the person is incapable of independent living. Early detection of AD will help to reducethe cognitive impairment and thus slow down the progressive nature of the disease by providing socialengagement and stimulating activities both mentally and physically [1]. The brain tissues startdegeneration much earlier than the clinical symptoms appear. So it is crucial to look at brain imaging toidentify the early-onset of AD. So, this project aims to develop a customized optimal diagnostic modelfor classifying the AD stages inclusive of early on-set from structural Magnetic Resonance Imaging. Development of adaptive pre-processing algorithms for structural Magnetic Resonance Imaging (MRI) To propose an effective segmentation method using attention maps to quantify the volumetricmeasurement of AD during all stages from MRI data. To extract optimal discriminative bio-imaging markers using the deep learning methodology. To develop multi-task and multimodal 3D deep neural network models for the classification of AD. To develop an ensemble of regression models to predict the prognostic index of AD
Effective start/end date1/02/2331/12/24

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 14 - Life Below Water
  • SDG 17 - Partnerships for the Goals


  • Alzheimer's disease
  • Deep learning


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