Détail du document
Identifiant
oai:arXiv.org:2310.16857
Sujet
Electrical Engineering and Systems... Computer Science - Machine Learnin...Auteur
Tan, PeiwenCatégorie
Computer Science
Année
2023
Date de référencement
01/11/2023
Mots clés
disease mri contrast alzheimerRésumé
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.
Tan, Peiwen, 2023, Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization
