Mamdani fuzzy inference system based on multi-textural biomarkers for Alzheimer’s stage detection

Alzheimer’s disease is a neurodegenerative brain disease that causes short-term memory loss. Recently, artificial intelligence (AI) is becoming important in the medical field and makes it possible to detect Alzheimer’s disease with greater accuracy. In this work, a novel Mamdani fuzzy inference system based on multi-textural biomarkers (MFIS-MTB) is proposed for the detection of Alzheimer stages. Initially, the MRI images are pre-processed to improve the quality of the image. The amygdala and hippocampus in the left hemisphere of the brain are segmented as the region of interest (ROI) from MRI scans of a small cohort. After normalising the ROI using µ±3σ, first-order histogram features (skewness and kurtosis) are retrieved. Additionally, two-dimensional wavelet features (max norm of the original image and diagonal detail coefficient) are extracted from the ROI. Based on these extracted textural markers, Mamdani fuzzy inference system (MFIS) is constructed with a least number of rules for the prognosis of the various phases of Alzheimer such as mild cognitive impairment (MCI), Alzheimer’s disease (AD) and normal controls (NC). The proposed MFIS-MTB model attains the accuracy of 96.13%, 94.73% and 93.11% for classifying AD vs NC, MCI vs NC and AD vs MCI respectively.