A pipelined, resource-efficient convolutional neural network architecture for detecting and diagnosing Alzheimer's disease using brain sMRI

The findings suggest that the RECNN framework offers a resource-efficient and accurate approach for AD detection using MRI data. By combining Gabor-based feature transformation, augmented data diversity, and advanced segmentation, the proposed method provides a scalable and clinically applicable tool for early diagnosis. Future work will extend the model to larger and more diverse datasets and explore hybrid architectures to further enhance diagnostic performance.