A Novel Self-Supervised Swin Transformer with Wavelet ‎Feature Extraction for Early Alzheimer’s Disease Recognition - Pushpakumari , Jupalli; Alekhya , Janapareddy Uttara; Nagalakshmidevi , Jakkapu; Maanasa , Mangu; Rani , Vaddadi Vasudha; Rao, M. Somasundara; Musinana, Chandra Sekhar (International Journal of Basic and Applied Sciences, 2025)
Alzheimer's disease is a structural and functional brain alterations that induces memory loss and thinking problems, which leads to worsen ‎over time. It’s crucial to identify the problem early so that treatment can be provided on time and care can be more effective. In this research, ‎we acquaint’s a new deep learning technique that uses MRI brain scans to find whether the person is suffering with Alzheimer’s disease or ‎not. Our methodology integrates self-supervised learning where the model trains the scanned images of MRI without labelled data, ad-‎vanced feature extraction techniques, and a modern vision transformer model. Initially, we used the FSL-BET tool to extracts only the relat-‎ed MRI images of the brains by using FSL (FMRIB Software Library). Then, we use a multi-level Wavelet Transform to acquire major ‎features from the images and mainly focusing on texture and frequency data that helps to observe disease-related changes in the brain. After ‎that, we trained a Swin Transformer model using self-supervised learning so it could acquire knowledge from the data without human label-‎ling. Now, we regulate the model to categorize the MRI scans into four classes: Mild Demented, Moderate Demented, Non-Demented, and ‎Very Mild Demented. Finally, this research showed that this model got a highest accuracy of 97.8%. It performed well when compared to ‎other popular deep learning models like VGG19 and ResNet, especially when only a small amount of labelled data was available. The pri-‎mary benefit of wavelet features with self-supervised learning is the enhancement of the model's performance. The research presents a ro-‎bust and scalable method for early detection of Alzheimer’s disease via brain MRI scans‎.
Keywords: Alzheimer's Disease; MRI; Skull Stripping; Wavelet Transform; Swin Transformer; Self-Supervised Learning; Deep Learning; Transfer Learning.