Innovative Approach to Alzheimer Disease Detection

This research shows a comparative evaluation of deep learning architectures of CNN, DenseNet-201, and ResNet50. We differentiate the stages of Alzheimer disease using (MRI) data from Kaggle Dataset. Using advanced machine learning and knowledge transfer methods, the study aims to enhance diagnostic precision and minimize the subjective nature of conventional diagnostic procedures. DenseNet-201 proves as the most effective model, achieving an accuracy of 86.4%, suggesting its viability for clinical adoption. The investigation highlights the importance of artificial intelligence-based medical imaging solutions in facilitating a prompt and accurate diagnosis, ultimately improving therapeutic outcomes and patient management. The model performance can be further improved by parameterizing the data-driven architecture implementing features reuse and efficient architectural design for deep learning systems. Next work will explore mixed architectural frameworks and use a variety of expanded datasets which improve both the robustness and explain how a machine learning model makes its predictions or decisions and the practical clinical use of these models.