School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
School of International Education, Hebei University of Technology, Finland Campus, Lahti-Lappeenranta Cities, Finland.
Curr Med Imaging. 2024;20:e15734056305633. doi: 10.2174/0115734056305633240603061644.
The increasing longevity of the population has made Alzheimer's disease (AD) a significant public health concern. However, the challenge of accurately distinguishing different disease stages due to limited variability within the same stage and the potential for errors in manual classification highlights the need for more precise approaches to classifying AD stages. In the field of deep learning, the ResNet50V2 model stands as a testament to its exceptional capabilities in image classification tasks.
The dataset employed in this study was sourced from Kaggle and consisted of 6400 MRI images that were meticulously collected and rigorously verified to assure their precision. The selection of images was conducted with great attention to detail, drawing from a diverse array of sources.
This study focuses on harnessing the potential of this model for AD classification, a task that relies on extracting disease-specific features. Furthermore, to achieve this, a multi-class classification methodology is employed, using transfer learning and fine-tuning of layers to adapt the pre-trained ResNet50V2 model for AD classification. Notably, the impact of various input layer sizes on model performance is investigated, meticulously striking a balance between capacity and computational efficiency. The optimal fine-tuning strategy is determined by counting layers within convolution blocks and selectively unfreezing and training individual layers after a designated layer index, ensuring consistency and reproducibility. Custom classification layers, dynamic learning rate reduction, and extensive visualization techniques are incorporated.
The model's performance is evaluated using accuracy, AUC, precision, recall, F1-score, and ROC curves. The comprehensive analysis reveals the model's ability to discriminate between AD stages. Visualization through confusion matrices aided in understanding model behavior. The rounded predicted labels enhanced practical utility.
This approach combined empirical research and iterative refinement, resulting in enhanced accuracy and reliability in AD classification. Our model holds promise for real-world applications, achieving an accuracy of 96.18%, showcasing the potential of deep learning in addressing complex medical challenges.
人口的长寿使得阿尔茨海默病（AD）成为一个重大的公共卫生关注点。然而，由于同一阶段内的变异性有限，以及手动分类可能存在误差，准确区分不同疾病阶段的挑战凸显了需要更精确的方法来对 AD 阶段进行分类。在深度学习领域，ResNet50V2 模型在图像分类任务中表现出色。
本研究使用的数据集来自 Kaggle，包含 6400 张 MRI 图像，这些图像经过精心收集和严格验证，以确保其精度。图像的选择非常注重细节，从各种来源中进行选择。
本研究重点利用该模型进行 AD 分类的潜力，这一任务依赖于提取疾病特异性特征。此外，为了实现这一目标，采用了多类分类方法，使用迁移学习和对层进行微调，以适应预训练的 ResNet50V2 模型进行 AD 分类。值得注意的是，研究了各种输入层大小对模型性能的影响，在容量和计算效率之间精心平衡。通过计算卷积块中的层并在指定的层索引后选择性地解冻和训练个别层，确定了最佳的微调策略，确保了一致性和可重复性。还纳入了自定义分类层、动态学习率降低和广泛的可视化技术。
使用准确性、AUC、精度、召回率、F1 分数和 ROC 曲线评估模型性能。综合分析揭示了模型区分 AD 阶段的能力。通过混淆矩阵进行可视化有助于理解模型行为。经过四舍五入的预测标签提高了实际应用的实用性。
该方法结合了实证研究和迭代改进，提高了 AD 分类的准确性和可靠性。我们的模型具有实际应用的潜力，达到了 96.18%的准确率，展示了深度学习在解决复杂医学挑战中的潜力。
