Robust hybrid deep learning models for Alzheimer’s progression detection

The prevalence of Alzheimer’s disease (AD) in the growing elderly population makes accurately predicting AD progression crucial. Due to AD’s complex etiology and pathogenesis, an effective medically practical solution is a challenging task. In this paper, we developed evaluated two novel hybrid deep learning architectures for detection. These models are based on fusion multiple bidirectional long short-term memory (BiLSTM) models. first architecture interpretable multitask regression model that predicts seven crucial cognitive scores patient 2.5 years after their last observations. predicted used build clinical decision support system glass-box model. This aims explore role multitasking producing more stable, robust, accurate results. second where features extracted from BiLSTM train machine classifiers. were comprehensively using different time series modalities 1371 subjects participated study neuroimaging initiative (ADNI). extensive, real-world experimental results over ADNI data help establish effectiveness practicality proposed