• A.Vinotha Vasuki
• P.Stella Rose Malar
• S.Muppudathi Sutha

Alzheimer's disease (AD) may be a progressive encephalopathy and therefore the commonest explanation for dementia in later life. It causes cognitive deterioration, eventually leading to inability to hold out activities of lifestyle. Alzheimer’s disease is an incurable, progressive neurological encephalopathy. Early diagnosis of Alzheimer’s disease can help with proper treatment and stop brain tissue damage. Several statistical and machine learning models are exploited by researchers for Alzheimer’s disease diagnosis. Detection of Alzheimer’s disease is exacting thanks to the similarity in Alzheimer’s disease resonance Imaging (MRI) data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. convolutional neural network for Alzheimer’s disease diagnosis using brain MRI data analysis. Experiments on the Alzheimer’s Disease specify that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it performance state-of-the-art methods for AD diagnosis

Alzheimer, Convolutional Neural Network, Deep learning, Image Fusion, Magnetic resonance imaging

"A Review on Multimodal Brain Image Fusion using Deep Learning for Alzheimer’s disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.e530-e533, May-2021, Available :http://www.jetir.org/papers/JETIR2105611.pdf

"A Review on Multimodal Brain Image Fusion using Deep Learning for Alzheimer’s disease", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppe530-e533, May-2021, Available at : http://www.jetir.org/papers/JETIR2105611.pdf