Atif Mehmood1,2, Zhonglong Zheng1,*, Rizwan Khan1, Ahmad Al Smadi3, Farah Shahid1,2, Shahid Iqbal4, Mutasem K. Alsmadi5, Yazeed Yasin Ghadi6, Syed Aziz Shah8, Mostafa M. Ibrahim7
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2081-2098, 2024, DOI:10.32604/cmc.2024.046869
Abstract Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e., shear range, rotation, zoom… More >
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