MobileNet-Based Transfer Learning: A Novel Approach for Improved Alzheimer's Disease Classification from Brain Imaging
Keywords:
Early Alzheimer’s disease, ADNI 5-class, Transfer Learning, MobileNet, DenseNet121, InceptionV3, Xception NetAbstract
Alzheimer's can result from various illnesses or incidents that severely impact normal brain functions. While Alzheimer's Disease (AD) does not have a specific treatment, initial Alzheimer's disease diagnosis requires neuroimaging, which is among the most promising disciplines for this purpose. It is possible to provide patients with the appropriate care if Alzheimer's disease is detected early. In numerous investigations, machine learning and statistical methods are used to diagnose AD. Deep Learning systems have shown effectiveness similar to that of humans in various fields. The research suggests utilizing deep learning methods such as transfer learning and fine-tuning for classifying and predicting AD. The neural networks DenseNet121, MobileNet, InceptionV3, and Xception are trained using the ADNI 5-class dataset. While the previous state-of-the-art technique achieves an overall accuracy of 86.57%, the proposed MobileNet architecture outperforms it with a validation accuracy of 98% with fine-tuning and 94% without fine-tuning. This research advances the classification of AD through utilizing pre-trained convolutional neural network models, promoting the exploration of unconventional indicators like eye-tracking, memory impairment, and concentration difficulty, amongst others.
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