Early detection of Alzheimer's disease using DTI-based neuro-fuzzy classification system
What is it about?
The method of AD diagnosis is a successful model in assisting the physicians in the screening treatment of AD. In this paper, the presented Fuzzy Expert System(FES) successfully identified the presence of AD in the patients by measuring three parameters, Apparent diffusion coefficient(ADC),Fractional anisotropy(FA), andGray matter(GM), of the brain. These are considered as input variables, and the use of the neuro-fuzzy soft computing method can assist physicians in the detection of AD at early stages. From the diagnosis of the disease, the physician can decide which stage of AD the patient has and take the necessary steps for earliest possible treatment. This paper presents an investigative study for the classification of AD based of a neuro-fuzzy system for detection of healthy or AD patients.
Why is it important?
The experimental results have proven that the diffusion tensor image(DTI)-based neuro-fuzzy classification system for pattern recognition gives a satisfactory performance with 100% accuracy results. We intend to extend this methodology for early detection of Parkinson’s disease or other dementia