Performance Evaluation of Deep Learning Models for Predicting Alzheimer’s

Alzheimer’s poses a significant challenge, disproportionately affecting the aging population with emotional and financial burdens. Effective treatment is impeded by the lack of etiology, which offers only symptomatic relief. Rapid exploration of disease mechanisms and early diagnosis is essential. A multidisciplinary research approach is increasingly needed in the context of global population ageing. The key to progress in knowledge and development of treatments lies in collaboration between scientists, health professionals, policy makers and the community. It has been shown that machine learning models like ResNet, DenseNet and VGG can enhance the accuracy of Alzheimer’s detection by using network convolution techniques on MRI scans. The ability to recognize subtle features may enable early, precise diagnosis and prompt treatment. The convergence of neural and artificial intelligence represents a beacon for hope in the continued fight against Alzheimer’s, promising improved patient outcomes.