Deep learning based screening framework for early Alzheimer's disease detection in MRI

Women are more severely impacted by Alzheimer's disease (AD), a progressive neurodegenerative illness, particularly in the age range of 45 to 60 years, when early signs are often incorrectly diagnosed. To improve the quality of life and slow the disease progression, it is crucial to detect the condition at this early stage. This research proposes a hybrid screening framework, integrating symptom level awareness through a structured survey along with deep learning based Magnetic Resonance Imaging (MRI) assessment. A curated dataset of 8511 preprocessed axial brain MRI image slices sourced from Kaggle's OASIS Database was used, categorized into Cognitively Normal, Moderate Impairment & Mild Cognitive Impairment classes. By utilizing feature extraction from three pretrained convolutional neural networks (AlexNet, ResNet50, and DenseNet121), performance was further improved using ensemble and stacking learning methods. Among the evaluated models, AlexNet, DenseNet121, and ResNet50 achieved accuracies of 0.94, 0.87, and 0.69 respectively, while the ensemble approach improved performance to 0.95 using soft voting and further to 0.97 with a stacking ensemble strategy. Integrated Gradients was used to generate saliency heatmaps that highlight clinically relevant neuroanatomical regions. Furthermore, a symptomatic survey of 169 women from Maharashtra was conducted and analyzed to facilitate scalable early screening. Thus, this study presents a comprehensive, interpretable, and feasible framework for the early detection of Alzheimer's.