Revolutionizing Dementia Diagnosis: Integrating DenseNet121 and SVM for Early Detection through MRI Analysis

Dementia, a debilitating condition affecting cognitive functions, necessitates early detection for effective management. This research introduces an innovative approach to diagnosed ementia by employing machine learning techniques, specifically integrating the Dense Net121 feature extractor and a Support Vector Machine (SVM) classifier.Utilizing a diverse MRI dataset obtained from Kaggle, comprising various stages of dementia, the model demonstrates promising results interms of precision and accuracy. Overcoming limitations of existing systems, the proposed method addresses temporal modeling challenges, imbalanced datasets, and high dimensionality associatedwith MRI data. The Flask web application developed facilitates user-friendly interaction for healthcare professionals, offering a practical tool for efficient dementia diagnosis. The study underscores the significance of early detection in influencing potential treatment strategies and contributes to the broader field of medical diagnostics.The findings highlight the effectiveness of DenseNet121 and SVM integration, show casing the potential of machine learning in conjunction with medical imaging for earlydementiadetection.