Deep Transfer Learning Models for Alzheimer’s Disease Classification using MRI Images
Keywords:
Alzheimer’s disease, Dementia, MRI images, Deep Learning, Transfer Learning, Optimization, ResamplingAbstract
Alzheimer’s Disease (AD) is considered as a neurological brain ailment that leads to the irreversible destruction of nerve cells in the brain which are connected to the tasks of memory and thinking process in humans. Dementia is the result of this disorder which globally effects nearly 50 million of people worldwide. Various Machine learning approaches have been a topic of research for diagnosing Alzheimer's disease using brain images like Magnetic Resonance Imaging (MRI). Recent breakthrough of Deep Learning technologies in computer vision has advanced this field of study. Nevertheless, there exists certain limitations such as dependency on huge amount of training data and the need for appropriate optimization method in deep neural network models. In this paper, we endeavor to address these concerns with deep transfer learning models, where modern pre-trained CNN models like VGG, RESNET, Inception and Xception are set with pre-trained weights obtained from large sized standard benchmark datasets consisting of natural images. The fully-connected layer is then re-trained with trivial number of MRI images. Furthermore we employ the use of data augmentation approach for learning from imbalanced datasets which effectively rises the performance of the transfer learning models.
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