Multi-Layer Feature Fusion-based Deep Multi-layer Depth Separable Convolution Neural Network for Alzheimer's Disease Detection
Santosh Kumar Tripathy, Divya Singh, Ankit Jaiswal
Abstract
Alzheimer's disease (AD) is a severe degenerative neurological disorder that can cause heart and respiratory dysfunction. Thus, early detection of such disease is highly required. Using MRI images, a number of deep models have been created to predict AD as artificial intelligence (AI) has advanced. However, these models suffer from limited representation in extracting fine-grained features from MRI images thereby performance is declined. The proposed model overcomes such limitation and presents a methodology for AD detection by proposing a novel multi-layer feature fusion-based deep multi-layer depth-wise separable convolution neural network (CNN). The proposed model enhances the quality of features by fusing multi-layer features. These features range from representations of low-level features to features at the object level. The fused multiscale features are used for predicting AD disease. Publicly available dataset is used to validate the model's performance. With an accuracy of 95.16 percent, the suggested model performs better than the current state of the art.