MR images-based Alzheimer's Detection and Prediction using Coherent Layering of AlexNET and InceptionV3

A problem associated with the neural system has been identified as the underlying cause of Alzheimer's disease (AD), a form of dementia. The field of study concerning this matter often relies on clinical data or questionnaires, which can yield conflicting outcomes due to individuals being hesitant to disclose information, possibly due to the nature of the illness or social stigma. Nevertheless, the consequence of early detection of AD cannot be excessive. The existence of recurring or identical characteristics poses a challenge to the efficacy of current computer-assisted procedures and models, which heavily depend on feature extraction. Consequently, achieving optimal results with a single model becomes a formidable task. This study proposes a enhanced way for the classification of Alzheimer's disease utilizing deep learning techniques. The method makes use of texture features and other pertinent information extracted from structural MRI data. Two deep learning models, namely AlexNet and InceptionV3, were employed to construct the predictive model and algorithm. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which categorizes data into five groups, including AD, CN, and MCI, was utilized to evaluate the suggested technique. The sophisticated methodologies employed in this study exhibited superior performance compared to conventional recommendation algorithms in terms of vital assessment parameters utilized to estimate the performance of the supervised classifier. According to the survey results, InceptionV3 emerged as the most successful classifier, achieving the highest accuracy (98.31%) and demonstrating exceptional performance in terms of precision (95.99%), recall (95.81%), and F1 score (95.8%).