Detection of Alzheimer disease using deep learning
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Abstract
Alzheimer's disease (AD) is a progressive disease that deteriorates the brain over time and has no existing cure and eventually leads to death. Its detection remains difficult. We present a deep learning-based technique for detecting Alzheimer's disease in its early stages. We propose a method for identifying the human brain surface in structural Magnetic Resonance Imaging (MRI) images. The combination of Active Contour (AC) and Prior Knowledge (PK) from the MRI is a major contribution in this method. We tested many pre-trained networks (AlexNet, VGG16, ResNet18, ResNet50 and MobileNetV2). We tested our method with the collection of OASIS database brain specialized to analyze Alzheimer MRI with 420 subjects; 210 normal and 210 Mild Cognitive Impairment. The majority of patients are aged between 18 to 96 years. The suggested architecture distinguishes between subjects with Normal Control (NC) and (MCI). The classification using the network ResNet50 achieves an exceptional test accuracy of 96.8%. The experimental findings demonstrate the suggested method's capacity to improve classification performance when compared to state-of-the-art approaches.
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