CNN-Driven Detection of Alzheimer’s Disease: A Deep Learning Approach with MRI Data from the Kaggle Dataset

Abstract

The neurological system is impacted by Alzheimer's disease (AD). As of right present, neither a medication nor a cure for Alzheimer's disease exist. An accurate diagnosis is critical for the early treatment of Alzheimer's disease (AD) because it allows patients to begin preventative medications prior to the development of permanent brain damage. This is due to the fact that the illness is currently in a very advanced stage. People with Alzheimer's disease can benefit from early diagnosis and effective treatment. The identification of Alzheimer's disease (AD) has been the subject of several research that have employed statistical and machine learning methods. A number of uses have allowed deep learning algorithms to demonstrate competence on par with humans in a number of fields. It is possible to detect Alzheimer's disease using magnetic resonance imaging (MRI) data and Deep Learning technologies for disease classification. Using deep learning algorithms for Alzheimer's disease classification has demonstrated encouraging outcomes. The combination of high precision, rapid processing, and generalisability over a wide variety of demographics is necessary for these techniques to be used in clinical settings. Using images from MRI scans that were trained with the Kaggle dataset, this study builds a system that can diagnose Alzheimer's disease using a fully convolutional network (CNN) architecture. It is feasible to assess the efficacy of each model by training them on the identical dataset.