Enhancing Alzheimer’s diagnosis: role of 3D convolutional neural networks in MRI analysis

Alzheimer’s Disease, a progressive neurodegenerative disorder, presents a significant challenge to global health, profoundly impacting individuals, families, and healthcare systems. Early and accurate diagnosis is essential for effective treatment and management. This study focuses on the use of 3D Convolutional Neural Networks to enhance the diagnostic process of Alzheimer’s using MRI scans, aiming to improve detection accuracy and contribute to better patient outcomes. By utilizing advanced imaging and neural network technologies, the research offers promising perspective about innovative approaches for Alzheimer’s detection. The performance of the proposed model is supported by thorough pre-processing and augmentation techniques. The model achieves a training accuracy of 93.03% with corresponding precision, recall, and AUC values of 92.51%, 92.21%, and 97.80%, respectively. The accuracy of the model is further confirmed during validation, maintaining high accuracy at 88.05%, with precision and recall at 87.50%.

• 主要作者: Pilacuan Medina, Genesis Michell (author)
• 格式: bachelorThesis
• 語言: eng
• 出版: 2024
• 主題: Enfermedad de Alzheimer; Redes Neuronales Convolucionales; Resonancia magnética; Alzheimer’s disease; Convolutional Neural Networks; Neuroimaging
• 在線閱讀: http://repositorio.yachaytech.edu.ec/handle/123456789/871