A Novel Spatial Attention Module (SAM) for Alzheimer's Detection from MRI Images

In this research we propose a novel Spatial Attention Module (SAM), incorporated on a Convolutional Neural Network (CNN) model, in order to detect Alzheimer's disease from imbalanced Magnetic Resonance Imaging (MRI) datasets. Early detection of Alzheimer's disease has garnered significant interest in the research community since the emergence of Deep Neural Networks, particularly Convolutional Neural Network (CNN) models. The potential impact of such advancements in healthcare is profound, and it has the potential to improve the accuracy of diagnosis. However, the performance of conventional CNN models is hindered due to class imbalance problems, since most of the medical datasets are found imbalanced in nature. The novelties of this work are as follows: (I) A very lightweight CNN model is proposed in which the number of convolutional filters are deployed considerably less and it is trained from scratch. (II) On top of this CNN, we have leveraged one novel attention module, in spatial domain, in which we have incorporated dilated convolutional filters instead of 7x7 filters. This Spatial Attention Module (SAM) has the ability to extract multi-scale features due to incorporating dilation rate 2, thus, it automatically produces a set of distinct features for Alzheimer's disease detection. As a consequence, this Spatial Attention Module (SAM) enables the CNN model to learn more variety of (multi-scale) features in order to generalize well, thus, it automatically mitigates the class imbalance problem to a certain extent. For the validity, we have conducted a 5-fold cross validation experiment by which we have generated 5 distinct MRI datasets from one 'AD-MRI dataset'. In each dataset, a different testing fold is chosen. Experimental results reveal that by the proposed framework we have achieved 97-98% testing accuracy consistently, throughout all the folds, hence, it proved the generalization ability of the proposed framework. Furthermore, our proposed model outperformed the majority of state-of-the-art models, and it surpassed the performance of a recent trends model, the Pooling based Vision Transformer (PiT), by a substantial margin. All the codes of several experiments along with their graph, and confusion matrices are available on a github link: https://anonymous.4open.science/r/Alzheimer-Disease-Detection/README.md