Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization
This work addresses the challenge of blurry and low-contrast MRI scans for Alzheimer's Disease diagnosis, offering a promising but incremental improvement in diagnostic accuracy.
The researchers tackled the problem of improving Alzheimer's Disease classification from MRI images by applying Fourier topological optimization to enhance image quality, resulting in a marked elevation in performance across CNN architectures like VGG16, ResNet50, InceptionV3, and Xception.
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.