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.
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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
Perspectives
I think this article makes a big opportunity for people and opening a big platform where the the physician can decide which stage of AD the patient has and take the necessary steps for earliest possible treatment.
Subrata Kar
Read the Original
This page is a summary of: A Novel Approach of Diffusion Tensor Visualization Based Neuro Fuzzy Classification System for Early Detection of Alzheimer’s Disease, Journal of Alzheimer s Disease Reports, January 2019, IOS Press,
DOI: 10.3233/adr-180082.
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