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    Deep learning-based model for accurate prediction of Alzheimer’s


    Alzheimer’s detection: Memory begins to weaken with age. It is usually caused by damage to the brain tissue. From a broader perspective, this mental disorder is called Alzheimer’s. According to the World Health Organization (WHO), the biggest contributor to Alzheimer’s is dementia, about 60 percent of the population, according to the Hindustan newspaper. Currently, the number of people infected with it worldwide is 24 million and their number is estimated to double every 20 years. Dementia is not the name of a disease, but it is the name of many diseases or a group of many symptoms.

    More precise and sensitive

    The disadvantage is that so far its dangers have not been properly predicted or treated. Under such circumstances, researchers have developed a deep learning-based model in which Alzheimer’s can be predicted with percent percent accuracy through brain imaging. The results of the study were published in the journal Diagnostics.

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    This method of predicting Alzheimer’s risk is based on MRI image analysis of 138 subjects, which is more accurate, sensitive and specific than the old method.

    The first signs of potential danger
    Ritis Muskelunas, a multimedia engineering researcher at Kaunas University of Technology (KTU), says doctors around the world are emphasizing raising awareness to identify early stages of Alzheimer’s so that infected people can benefit better from treatment. The first sign of a potential risk factor for Alzheimer’s is the stage between mild forgetfulness (MCI), aging and the expected cognitive decline in dementia.

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    According to previous research, functional magnetic resonance imaging (FMRI) can be used to identify areas of the brain that are associated with a potential risk of Alzheimer’s. There are no obvious symptoms in the early stages of MCI, but in some cases it can be detected by neuroimaging.

    Image analysis will be faster
    Although it is possible to detect Alzheimer’s-related changes through manual analysis of FMRI images, this requires not only specific information, but also timeliness. Although it can be accelerated by other methods of deep learning and artificial intelligence.

    Developed on the basis of deep learning, this model has been developed in collaboration with researchers in the field of artificial intelligence in Lithuania. In this, effective MRI has been classified by improving Resnet 18 (remaining neural network). These images were divided into 6 different sections, from MCI to Alzheimer’s disease status. The accuracy of this model was 99.95 percent.

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