Abstract—Structural Magnetic Resonance Imaging (sMRI) has investigated several neurological illnesses, and it has been mapped to unhealthy areas in the brain. Alzheimer’s Disease (AD) individuals must be identified as soon as possible so that treatment may begin. Recent research has focused on applying Machine Learning (ML) techniques to segment the brain’s structure and categorize AD. Clonal Selection (CS) theory has effectively achieved the goal of categorization and optimization. An Adaptive Clonal Selection (ACS) technique used to categorize sMRI scans into multi-class such as Cognitive Normal (CN), Mild Cognitive Impairment (MCI), and Pure AD categories. The proposed ACS characterizes essential features of the immunological response. This provides support for hypothesis that antigen can only mature inside subset of cells that receive it, as opposed to the rest of the body. Comparable to evolutionary computation relying on mutations, this method excelled at focusing on the idea of clonal expansion and the development of affinity. Proposed ACS technique introduces basic criteria from concept of clonal expansion, assist in creation of highly effective strategies for identifying template matches for aforementioned CN, MCI, and AD. The suggested ACS method outperforms the state-of-the-art methods in aspects of classification and detection accuracy by around 99%.
 
Keywords—Alzheimer’s Disease (AD), Magnetic Resonance Imaging (sMRI), Artificial Immune System (AIS), Enhanced Fuzzy K Nearest Neighbor (EFKNN), Adaptive Neuro-Fuzzy Inference System (ANFIS)


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