  This research underscores the efficacy of Fourier topological optimization in
refining MRI imagery, thereby bolstering the classification precision of
Alzheimer's Disease through convolutional neural networks. Recognizing that MRI
scans are indispensable for neurological assessments, but frequently grapple
with issues like blurriness and contrast irregularities, the deployment of
Fourier topological optimization offered enhanced delineation of brain
structures, ameliorated noise, and superior contrast. The applied techniques
prioritized boundary enhancement, contrast and brightness adjustments, and
overall image lucidity. Employing CNN architectures VGG16, ResNet50,
InceptionV3, and Xception, the post-optimization analysis revealed a marked
elevation in performance. Conclusively, the amalgamation of Fourier topological
optimization with CNNs delineates a promising trajectory for the nuanced
classification of Alzheimer's Disease, portending a transformative impact on
its diagnostic paradigms.
