ADSENet: Alzheimer’s disease classification using multiple deep residual learning with squeeze and excitation networks

Alzheimer’s disease is a degenerative neurological condition marked by the systematic deterioration and ultimate reduction in the size of nerve cells within the brain. Although it is highly challenging to cure, the most successful approach is to initiate therapy at an early stage of the illness. Timely identification of Alzheimer’s disease increases individuals’ likelihood of receiving effective therapy, making it crucial to obtain the condition promptly. Neuroimaging is a method used to classify Alzheimer’s disease based on the identification of structural abnormalities in the brain. This image-based categorization job is ideally suited to contemporary computer vision. The primary diagnostic approach is the evaluation of magnetic resonance imaging (MRI) images of the patient’s brain. We proposed a deep learning model that consists of multiple residual learning blocks with squeeze excitation networks, which can aid in early detection from brain MRI images with a high. Our approach uses a deep learning model architecture named ADSENet, which was trained on a collection of MRI images. The results indicate that volumetric measurements are highly effective in diagnosing Alzheimer’s disease with outstanding accuracy, so offering the possibility of early detection of the disease. This method achieves a test accuracy of 99.30% in the classification of MRI scans into four phases of Alzheimer’s disease, surpassing previously considered state-of-the-art approaches.