A Comparative Analysis of Alzheimer’s Disease Detection using Deep Learning

Alzheimer’s is one of the progressively debilitating conditions, and it currently affects millions of people worldwide with no definitive medication for treatment. Understanding the nature of this disease is very important and stands to achieve early intervention and management. The development of models to recognize individuals having Alzheimer’s based on neuroimaging data, particularly MRI scans, has thus become accessible through recently advanced machine learning and deep learning algorithms. This paper does a comparison of some of the leading algorithms. Models used for research in this study are Support Vector Machines, Convolutional Neural Networks, 3D Convolutional Neural Networks, and hybrid pre-trained models. There’s model differentiation which resulted in pointing out the gap in the system and weaknesses of each approach. Apart from this, the discovery of the Explainable AI (XAI) concept regarding insight in the predictions of the algorithms has also been emphasized recently. As a result, XAI can improve the interpretability of AI systems. With this perspective, the degree of insight could lead toward better outcomes as far as diagnosis and responses in clinical environments are concerned. Given the importance of the paper, it will suffice to contribute to further refinements of diagnostic methodologies for AD and inform future studies on the subject.