A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection using Brain MRIs

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD worsens over time and greatly impacts patients' life and affects their important mental functions, including thinking, the ability to carry on a conversation, and judgment and response to environment. Clinically, there is no single test to effectively diagnose Alzheimer disease. However, computed tomography (CT) and magnetic resonance imaging (MRI) scans can be used to help in AD diagnosis by observing critical changes in the size of different brain areas, typically parietal and temporal lobes areas. In this work, an integrative mulitresolutional ensemble deep learning-based framework is proposed to achieve better predictive performance for the diagnosis of Alzheimer disease. Unlike ResNet, DenseNet and their variants proposed pipeline utilizes PartialNet in a hierarchical design tailored to AD detection using brain MRIs. The advantage of the proposed analysis system is that PartialNet diversified the depth and deep supervision. Additionally, it also incorporates the properties of identity mappings which makes it powerful in better learning due to feature reuse. Besides, the proposed ensemble PartialNet is better in vanishing gradient, diminishing forward-flow with low number of parameters and better training time in comparison to its counter network. The proposed analysis pipeline has been tested and evaluated on benchmark ADNI dataset collected from 379 subjects patients. Quantitative validation of the obtained results documented our framework's capability, outperforming state-of-the-art learning approaches for both multi-and binary-class AD detection.