Focal-DenseNet: A Risk Assessment Framework for Alzheimer's Disease in Heterogeneous MRI Data

Alzheimer's disease (AD) is a progressive neurodegenerative disease of the nervous system, which has become an important public health issue attracting global attention. However, its exact causes and pathogenesis have not been fully elucidated, and the existing treatment methods and intervention measures have limited efficacy. Therefore, how to establish a scientific and efficient risk assessment mechanism has become the key to the prevention and treatment of AD. Aiming at this problem, this paper optimizes the convolutional neural network structure of DenseNet and proposes a Focal-DenseNet model that integrates the focal loss function for the risk assessment and diagnostic prediction of AD. First of all, preprocess the collected data to ensure the data quality for subsequent analysis. Secondly, establish the Focal-DenseNet model. Finally, use the model for training and testing. The test results show that the test set accuracy of the model reaches 98.98%, indicating that the model performs well. In addition, this paper also compares the proposed model with the DenseNet model without using the focal loss function and other common deep learning models (such as VGG16, ResNet, etc.). The results show that the model in this paper exhibits superiority in multiple performance indicators. In particular, it has achieved high scores in key indicators such as accuracy, AUC (the area under the receiver operating characteristic curve), precision, and recall. This study provides an efficient technical support for the early risk assessment of AD, and holds significant clinical application value and academic reference significance for improving the prevention and treatment level of AD and reducing the global public health burden.