Lesion Areas in Brain MRI Images of Alzheimer's Disease Patients Using Convolutional Neural Networks

Early diagnosis of Alzheimer's disease, a progressively worsening neurodegenerative disorder, is critical to slowing the progression of the disease. In this study, a Convolutional Neural Network (CNN) model is used for indepth analysis of brain MRI (Magnetic Resonance Imaging) images of Alzheimer's disease patients with the aim of automatically identifying and quantitatively evaluating lesion areas. In terms of specific methods, MRI images are first preprocessed, including denoising, normalization and enhancement, to improve image quality and data consistency. Subsequently, a deep convolutional neural network model is designed and trained to accurately label the lesion areas in the brain through multi-layer feature extraction and learning. Finally, a cross-validation approach is used to assess the accuracy and generalization ability of the model. The neural network is on par with specialized physicians in terms of missed diagnoses. The images were obtained from a database of brain MRI images of Alzheimer's patients randomly selected at the hospital. Out of 100 cases, the neural network correctly diagnoses 90 cases, while the radiologist correctly diagnoses 85 cases. This shows that the accuracy of the neural network (90%) is higher than that of radiologists (85%). The high-precision image analysis technology in this study not only assists physicians in diagnosing and evaluating Alzheimer's disease more accurately, but also helps medical researchers to gain a deeper understanding of the developmental mechanisms of the disease, which in turn promotes the innovation and development of therapeutic strategies for Alzheimer's disease. In addition, the method proposed in this study can be extended to the study of other types of neurodegenerative diseases, which has a wide range of application prospects.