Deep learning Based Alzheimer’s Disease Detection Model using Multimodal Data
Keywords:
Deep learning, pre-processing, multimodal feature processing, optimization algorithm, weighted autoencoder, deep fusion strategy, and multi-class classification.Abstract
Alzheimer’s disease (AD) is considered an irreversible and progressive neurodegenerative disease that causes mortality in older people. Thus, early AD detection offers a significant role in controlling and preventing its progression. This paper proposes an DL-based AD detection model (DL-ADDM) based on deep learning (DL) together with multimodal feature processing that includes IoT data and image data for early-stage AD detection. The inputs of different data formats are fed into a multimodality-based integrated classifier model (MICM). For the generation of IoT data modal, pre-processing is carried through missing value imputation, data normalization and validation. Then, the features of the IoT data are extracted through a weighted auto encoder (WAE) along with an Attention-based Bi-directional long short-term memory (Attn_BiLSTM). In the image model, the pre-processing of images is performed using a Min-Max Gaussian filtering (M-squared GF) approach. Then, the informative features are extracted using the convolutional capsule network_chimp optimization algorithm (Conv_Capsnet_COA). Afterwards, the feature vectors obtained from the multimodal data processing are fused using the deep fusion strategy (DFS). Finally, the multimodal data output is classified through the softmax unit. The proposed DL-ADDM is simulated on the Python platform and the performances are evaluated using the main cognitive testing dataset (IoT data) and the Kaggle dataset (image data). The simulated outcomes showed that the proposed model had reached a maximum accuracy of 99.5% for CN/AD, 99.3% for CN/MCI and 99.6% for MCI/AD class.
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