An Alzheimer’s disease classification method using fusion of features from brain Magnetic Resonance Image transforms and deep convolutional networks

Alzheimer’s is a progressive and irreversible brain degenerative disorder, and presenting an accurate early-stage diagnosis tool is vital for preventing disease progression. The previous research considered the features only from the brain Magnetic Resonance Image (MRI) or its transforms. This study presents two efficient fusion schemes to combine the deep features obtained from the brain MR image and its transforms. We consider the Orthogonal Ripplet II Transform (ORT-II), the Two-Dimensional Discrete Orthonormal Stockwell Transform (2D DOST), and the original brain MR image. We present the decision- and feature-level fusion schemes to combine the information of different inputs. In decision-level fusion, Convolutional Neural Networks (CNNs) separately classify each matrix, where all CNNs have the same structure, and the majority role makes the final decision. In feature-level fusion, the same CNNs extract each matrix’s features and then concatenate them to construct the feature vector. Since this vector contains many features, we employ Neighborhood Component Analysis (NCA) to select the more relevant features for classification to determine the level of dementia. We consider the different CNN models and classifiers to classify the brain MR images in three two-class scenarios. The results demonstrate that EfficientNet-B7 with an Artificial Neural Network (ANN) provides the highest accuracy. Also, feature-level fusion reaches a higher accuracy compared to the decision-level one.