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Abstract

The accurate diagnosis of Alzheimer’s disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease Mild Cognitive Impairment (MCI), the early stage of Alzheimer’s (AD), is used for clinical trials. Different imaging techniques have been used to help diagnose the disease. A few of them are magnetic resonance imaging (MRI), computed tomography, positron emission tomography. These features from MRI and PET images are extracted for diagnosis. Fusion of these features can improve the accuracy of diagnosis. Due to some defects of original PCNN for data fusion, the dual channel PCNN is used to implement multimodal image fusion. This method gives additional features for better diagnosis of AD.

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How to Cite
P.Sounthariya, & A.Tamilarasi. (2018). The early diagnosis of alzheimer’s disease using deep learning . International Journal of Intellectual Advancements and Research in Engineering Computations, 6(2), 1064–1072. Retrieved from https://ijiarec.com/ijiarec/article/view/822