Alzheimer’s Disease Detection Model Using Rotation Invariant DTCWT Features and Recurrent Neural Network With a Combination of LSTM and GRU - Topannavar, Preeti; Yadav, D. M.; Bendre, Varsha (Communications in Mathematics and Applications, 2023)
The characteristic features of brain magnetic resonance image (MRI) scans of Alzheimer’s disease (AD) patients and normal controls (NC) are difficult to differentiate because of very similar brain patterns and image intensities at the initial stages. Thus, it is difficult to detect AD at earlier stages. The earlier detection can lead to earlier medication for the patients to avoid further and permanent damage to their brains. Thus, computer-aided systems for early Alzheimer’s disease detection are needed. For this purpose, the lossless feature extraction method combined with feature reduction using a selection approach is one of the best possible solutions presented in this paper. The dataset used for experimentation for AD detection is a combination of MRI images with very mild and mild cognitive impairments. The 2D dual-tree complex wavelet transform (DTCWT) is used for feature extraction. A recurrent neural network (RNN) architecture is used for classification purposes. The long short-term memory (LSTM) and gated recurrent unit (GRU) are used in combination in the proposed architecture. The performance evaluation of combinations of LSTM and GRU and individual layers is performed, in which LSTM sandwiched between two GRU in the proposed model shows better performance.