Adaptive SE-attention ConvTransformer for Alzheimer’s detection using MRI images

Alzheimer’s disease (AD) is a neurodegenerative disorder that damages brain cells if not recognized and treated early. AD is a chronic disease, but proper treatment in the early stage can help to manage AD. Deep learning models can examine magnetic resonance imaging (MRI) scans to predict significant modifications in the brain associated with AD. Thus, this work develops an automated model for AD detection using deep learning. Initially, MRI images are collected from standard datasets, such as the OASIS-based Kaggle repository and the augmented Alzheimer MRI dataset. These images are then given to the Adaptive Squeeze-and-Excitation Attention Module-based Conv-Transformer Network (ASEA-ConvTNet) for the AD detection process. The developed ASEA-ConvTNet incorporated the convolutional neural network (CNN) element with a transformer network to achieve global contextual data. Moreover, the Squeeze-and-Excitation (SE) block is used to develop a feature response in a channel manner to learn the interdependency between feature details. In this, the ‘Adaptive’ specifies the utilization of the renovated constant parameter of flower fertilization optimization (RCP-FFO) to optimize the model parameters during training. The RCP-FFO-based optimization process is influenced by the network to get the fine-tuned parameter value that helps to reduce the parameter usage and improve the performance. Thus, from the developed model, the AD-detected outcome is achieved. Then, performance and comparative analyses are performed using various measures and different algorithms. The developed system achieves accuracy rates of 94.9% and 93.5% on dataset 1 and dataset 2, respectively, based on the 3rd fold of a 5-fold cross validation method.