Alzheimer's disease (AD), responsible for 60–80% of dementia cases characterized by irreversible brain damage and cognitive decline, predominantly affects individuals in their early 60s. With the increasing global prevalence of AD, there is a growing need for reliable detection systems using brain imaging. However, existing methods are primarily offline or static dataset-based, which limits the ability to adapt to changes in new data. Additionally, for fuzzy systems, predefined parameterized membership functions are ineffective in addressing changing patterns in streaming data. To address this, we propose a tree-based self-organizing fuzzy (TSOF) streaming data classification method for AD detection, which operates in two stages: offline, using a fixed dataset, and online, using streaming data. In the offline stage, a tree-based ranking process is employed to generate data-clouds and AnYa-type fuzzy rules from static data. This data-cloud has the ability to adopt changes in streaming data and offers a more objective representation of the data. In the online phase, the system updates its AnYa-type fuzzy rules and data-clouds to accommodate the dynamic nature of streaming data. A data preprocessing framework is introduced, incorporating segmentation, data augmentation, feature extraction, and concatenation. The proposed method is evaluated on the MPRAGE structural MRI dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which demonstrates superior performance on classification accuracy and training time compared to other state-of-the-art techniques. The proposed TSOF is available at https://github.com/Sajaldeb25/TSOF.
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